diff --git a/integrations/mongodb_atlas/src/haystack_integrations/components/retrievers/mongodb_atlas/embedding_retriever.py b/integrations/mongodb_atlas/src/haystack_integrations/components/retrievers/mongodb_atlas/embedding_retriever.py
new file mode 100644
index 000000000..3372638b5
--- /dev/null
+++ b/integrations/mongodb_atlas/src/haystack_integrations/components/retrievers/mongodb_atlas/embedding_retriever.py
@@ -0,0 +1,94 @@
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, List, Literal, Optional
+
+from haystack import component, default_from_dict, default_to_dict
+from haystack.dataclasses import Document
+from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+from haystack_integrations.document_stores.mongodb_atlas.document_store import METRIC_TYPES
+
+
+@component
+class MongoDBAtlasEmbeddingRetriever:
+    """
+    Retrieves documents from the MongoDBAtlasDocumentStore by embedding similarity.
+
+    Needs to be connected to the MongoDBAtlasDocumentStore.
+    """
+
+    def __init__(
+        self,
+        *,
+        document_store: MongoDBAtlasDocumentStore,
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: int = 10,
+        similarity: Literal["euclidean", "cosine", "dotProduct"] = "cosine",
+    ):
+        """
+        Create the MongoDBAtlasDocumentStore component.
+
+        :param document_store: An instance of MongoDBAtlasDocumentStore.
+        :param filters: Filters applied to the retrieved Documents. Defaults to None.
+        :param top_k: Maximum number of Documents to return, defaults to 10.
+        :param similarity: The similarity function to use when searching for similar embeddings. 
+            Defaults to "cosine". Valid values are "cosine", "euclidean", "dotProduct".
+        """
+        if not isinstance(document_store, MongoDBAtlasDocumentStore):
+            msg = "document_store must be an instance of MongoDBAtlasDocumentStore"
+            raise ValueError(msg)
+
+        if similarity not in METRIC_TYPES:
+            msg = f"vector_function must be one of {METRIC_TYPES}"
+            raise ValueError(msg)
+
+        self.document_store = document_store
+        self.filters = filters or {}
+        self.top_k = top_k
+        self.similarity = similarity
+
+    def to_dict(self) -> Dict[str, Any]:
+        return default_to_dict(
+            self,
+            filters=self.filters,
+            top_k=self.top_k,
+            similarity=self.similarity,
+            document_store=self.document_store.to_dict(),
+        )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "MongoDBAtlasEmbeddingRetriever":
+        data["init_parameters"]["document_store"] = default_from_dict(
+            MongoDBAtlasDocumentStore, data["init_parameters"]["document_store"]
+        )
+        return default_from_dict(cls, data)
+
+    @component.output_types(documents=List[Document])
+    def run(
+        self,
+        query_embedding: List[float],
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: Optional[int] = None,
+        similarity: Optional[Literal["euclidean", "cosine", "dotProduct"]] = None,
+    ):
+        """
+        Retrieve documents from the MongoDBAtlasDocumentStore, based on their embeddings.
+
+        :param query_embedding: Embedding of the query.
+        :param filters: Filters applied to the retrieved Documents.
+        :param top_k: Maximum number of Documents to return.
+        :param similarity: The similarity function to use when searching for similar embeddings.
+            Defaults to the value provided in the constructor. Valid values are "cosine", "euclidean", "dotProduct".
+        :return: List of Documents similar to `query_embedding`.
+        """
+        filters = filters or self.filters
+        top_k = top_k or self.top_k
+        similarity = similarity or self.similarity
+
+        docs = self.document_store.embedding_retrieval(
+            query_embedding=query_embedding,
+            filters=filters,
+            top_k=top_k,
+            similarity=similarity,
+        )
+        return {"documents": docs}
diff --git a/integrations/mongodb_atlas/src/haystack_integrations/document_stores/mongodb_atlas/document_store.py b/integrations/mongodb_atlas/src/haystack_integrations/document_stores/mongodb_atlas/document_store.py
index 68c02522e..2ecf4308d 100644
--- a/integrations/mongodb_atlas/src/haystack_integrations/document_stores/mongodb_atlas/document_store.py
+++ b/integrations/mongodb_atlas/src/haystack_integrations/document_stores/mongodb_atlas/document_store.py
@@ -5,6 +5,7 @@
 import re
 from typing import Any, Dict, List, Optional, Union
 
+import numpy as np
 from haystack import default_from_dict, default_to_dict
 from haystack.dataclasses.document import Document
 from haystack.document_stores.errors import DuplicateDocumentError
@@ -18,6 +19,9 @@
 logger = logging.getLogger(__name__)
 
 
+METRIC_TYPES = ["euclidean", "cosine", "dotProduct"]
+
+
 class MongoDBAtlasDocumentStore:
     def __init__(
         self,
@@ -25,6 +29,7 @@ def __init__(
         mongo_connection_string: Secret = Secret.from_env_var("MONGO_CONNECTION_STRING"),  # noqa: B008
         database_name: str,
         collection_name: str,
+        vector_search_index: str,
         recreate_collection: bool = False,
     ):
         """
@@ -38,7 +43,11 @@ def __init__(
             This value will be read automatically from the env var "MONGO_CONNECTION_STRING".
         :param database_name: Name of the database to use.
         :param collection_name: Name of the collection to use.
-        :param recreate_collection: Whether to recreate the collection when initializing the document store.
+        :param vector_search_index: The name of the vector search index to use for vector search operations.
+            Create a vector_search_index in the Atlas web UI and specify the init params of MongoDBAtlasDocumentStore. \
+            See https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#std-label-avs-create-index
+        :param recreate_collection: Whether to recreate the collection when initializing the document store. Defaults
+            to False.
         """
         if collection_name and not bool(re.match(r"^[a-zA-Z0-9\-_]+$", collection_name)):
             msg = f'Invalid collection name: "{collection_name}". It can only contain letters, numbers, -, or _.'
@@ -49,6 +58,7 @@ def __init__(
 
         self.database_name = database_name
         self.collection_name = collection_name
+        self.vector_search_index = vector_search_index
         self.recreate_collection = recreate_collection
 
         self.connection: MongoClient = MongoClient(
@@ -75,9 +85,10 @@ def to_dict(self) -> Dict[str, Any]:
             mongo_connection_string=self.mongo_connection_string.to_dict(),
             database_name=self.database_name,
             collection_name=self.collection_name,
+            vector_search_index=self.vector_search_index,
             recreate_collection=self.recreate_collection,
         )
-
+    
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "MongoDBAtlasDocumentStore":
         """
@@ -159,3 +170,83 @@ def delete_documents(self, document_ids: List[str]) -> None:
         if not document_ids:
             return
         self.collection.delete_many(filter={"id": {"$in": document_ids}})
+
+    def embedding_retrieval(
+        self,
+        query_embedding: np.ndarray,
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: int = 10,
+        similarity: str = "cosine",
+        scale_score: bool = True,
+    ) -> List[Document]:
+        """
+        Find the documents that are most similar to the provided `query_emb` by using a vector similarity metric.
+
+        :param query_emb: Embedding of the query
+        :param filters: optional filters (see get_all_documents for description).
+        :param top_k: How many documents to return.
+        :param similarity: The similarity function to use. Currently supported: `dotProduct`, `cosine` and `euclidean`.
+        :param scale_score: Whether to scale the similarity score to the unit interval (range of [0,1]).
+                            If true (default) similarity scores (e.g. cosine or dot_product) which naturally have a 
+                            different value range will be scaled to a range of [0,1], where 1 means extremely relevant.
+                            Otherwise raw similarity scores (e.g. cosine or dot_product) will be used.
+        """
+        if similarity not in METRIC_TYPES:
+            raise ValueError(
+                "MongoDB Atlas currently supports 'dotProduct', 'cosine' and 'euclidean' similarity metrics. \
+                Please set 'similarity' to one of the above."
+            )
+        
+        query_embedding = np.array(query_embedding).astype(np.float32)
+
+        if similarity == "cosine":
+            self.normalize_embedding(query_embedding)
+
+        pipeline = [
+            {
+                "$vectorSearch": {
+                    "index": self.vector_search_index,
+                    "queryVector": query_embedding.tolist(),
+                    "path": "embedding",
+                    "numCandidates": 100,
+                    "limit": top_k,
+                }
+            }
+        ]
+
+        filters = haystack_filters_to_mongo(filters)
+        if filters is not None:
+            pipeline.append({"$match": filters})
+            
+        pipeline.append({"$set": {"score": {"$meta": "vectorSearchScore"}}})
+        documents = list(self.collection.aggregate(pipeline))
+
+        if scale_score:
+            for doc in documents:
+                doc["score"] = self.scale_to_unit_interval(doc["score"], similarity)
+
+        documents = [self.mongo_doc_to_haystack_doc(doc) for doc in documents]
+        return documents
+    
+    def mongo_doc_to_haystack_doc(mongo_doc: Dict[str, Any]) -> Document:
+        """
+        Converts the dictionary coming out of MongoDB into a Haystack document
+
+        :param mongo_doc: A dictionary representing a document as stored in MongoDB
+        :return: A Haystack Document object
+        """
+        mongo_doc.pop("_id", None)
+        return Document.from_dict(mongo_doc)
+    
+    def normalize_embedding(self, emb: np.ndarray) -> None:
+        """
+        Performs L2 normalization of a 1D embeddings vector **inplace**. 
+        """
+        norm = np.sqrt(emb.dot(emb))  # faster than np.linalg.norm()
+        if norm != 0.0:
+            emb /= norm
+
+    def scale_to_unit_interval(self, score: float, similarity: Optional[str]) -> float:
+        if similarity == "cosine":
+            return (score + 1) / 2
+        return float(1 / (1 + np.exp(-score / 100)))
diff --git a/integrations/mongodb_atlas/tests/test_document_store.py b/integrations/mongodb_atlas/tests/test_document_store.py
index 07aeabc0b..274f90a95 100644
--- a/integrations/mongodb_atlas/tests/test_document_store.py
+++ b/integrations/mongodb_atlas/tests/test_document_store.py
@@ -20,9 +20,10 @@ def document_store(request):
     store = MongoDBAtlasDocumentStore(
         database_name="haystack_integration_test",
         collection_name=request.node.name + str(uuid4()),
+        vector_search_index="vector_search_index",
+        recreate_collection=True,
     )
-    yield store
-    store.collection.drop()
+    return store
 
 
 @pytest.mark.skipif(
@@ -56,6 +57,7 @@ def test_to_dict(self, _):
         document_store = MongoDBAtlasDocumentStore(
             database_name="database_name",
             collection_name="collection_name",
+            vector_search_index="vector_search_index",
         )
         assert document_store.to_dict() == {
             "type": "haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore",
@@ -70,6 +72,7 @@ def test_to_dict(self, _):
                 "database_name": "database_name",
                 "collection_name": "collection_name",
                 "recreate_collection": False,
+                "vector_search_index": "vector_search_index",
             },
         }
 
@@ -88,6 +91,7 @@ def test_from_dict(self, _):
                     },
                     "database_name": "database_name",
                     "collection_name": "collection_name",
+                    "vector_search_index": "vector_search_index",
                     "recreate_collection": True,
                 },
             }
@@ -95,4 +99,5 @@ def test_from_dict(self, _):
         assert docstore.mongo_connection_string == Secret.from_env_var("MONGO_CONNECTION_STRING")
         assert docstore.database_name == "database_name"
         assert docstore.collection_name == "collection_name"
+        assert docstore.vector_search_index == "vector_search_index"
         assert docstore.recreate_collection
diff --git a/integrations/mongodb_atlas/tests/test_embedding_retrieval.py b/integrations/mongodb_atlas/tests/test_embedding_retrieval.py
new file mode 100644
index 000000000..50db809ba
--- /dev/null
+++ b/integrations/mongodb_atlas/tests/test_embedding_retrieval.py
@@ -0,0 +1,106 @@
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+from typing import List
+from uuid import uuid4
+import os
+
+import pytest
+from haystack.dataclasses.document import Document
+from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+from numpy.random import rand
+
+
+@pytest.fixture
+def document_store(request):
+    store = MongoDBAtlasDocumentStore(
+        database_name="haystack_integration_test",
+        collection_name=request.node.name + str(uuid4()),
+        vector_search_index="vector_search_index",
+        recreate_collection=True,
+    )
+    return store
+
+
+@pytest.mark.skipif(
+    "MONGO_CONNECTION_STRING" not in os.environ,
+    reason="No MongoDB Atlas connection string provided",
+)
+class TestEmbeddingRetrieval:
+    
+    def test_embedding_retrieval_cosine_similarity(self, document_store: MongoDBAtlasDocumentStore):
+        query_embedding = [0.1] * 768
+        most_similar_embedding = [0.8] * 768
+        second_best_embedding = [0.8] * 700 + [0.1] * 3 + [0.2] * 65
+        another_embedding = rand(768).tolist()
+
+        docs = [
+            Document(content="Most similar document (cosine sim)", embedding=most_similar_embedding),
+            Document(content="2nd best document (cosine sim)", embedding=second_best_embedding),
+            Document(content="Not very similar document (cosine sim)", embedding=another_embedding),
+        ]
+
+        document_store.write_documents(docs)
+
+        results = document_store.embedding_retrieval(
+            query_embedding=query_embedding, top_k=2, filters={}, similarity="cosine"
+        )
+        assert len(results) == 2
+        assert results[0].content == "Most similar document (cosine sim)"
+        assert results[1].content == "2nd best document (cosine sim)"
+        assert results[0].score > results[1].score
+
+    def test_embedding_retrieval_dot_product(self, document_store: MongoDBAtlasDocumentStore):
+        query_embedding = [0.1] * 768
+        most_similar_embedding = [0.8] * 768
+        second_best_embedding = [0.8] * 700 + [0.1] * 3 + [0.2] * 65
+        another_embedding = rand(768).tolist()
+
+        docs = [
+            Document(content="Most similar document (dot product)", embedding=most_similar_embedding),
+            Document(content="2nd best document (dot product)", embedding=second_best_embedding),
+            Document(content="Not very similar document (dot product)", embedding=another_embedding),
+        ]
+
+        document_store.write_documents(docs)
+
+        results = document_store.embedding_retrieval(
+            query_embedding=query_embedding, top_k=2, filters={}, similarity="dotProduct"
+        )
+        assert len(results) == 2
+        assert results[0].content == "Most similar document (dot product)"
+        assert results[1].content == "2nd best document (dot product)"
+        assert results[0].score > results[1].score
+
+    
+    def test_embedding_retrieval_euclidean(self, document_store: MongoDBAtlasDocumentStore):
+        query_embedding = [0.1] * 768
+        most_similar_embedding = [0.8] * 768
+        second_best_embedding = [0.8] * 700 + [0.1] * 3 + [0.2] * 65
+        another_embedding = rand(768).tolist()
+
+        docs = [
+            Document(content="Most similar document (euclidean)", embedding=most_similar_embedding),
+            Document(content="2nd best document (euclidean)", embedding=second_best_embedding),
+            Document(content="Not very similar document (euclidean)", embedding=another_embedding),
+        ]
+
+        document_store.write_documents(docs)
+
+        results = document_store.embedding_retrieval(
+            query_embedding=query_embedding, top_k=2, filters={}, similarity="euclidean"
+        )
+        assert len(results) == 2
+        assert results[0].content == "Most similar document (euclidean)"
+        assert results[1].content == "2nd best document (euclidean)"
+        assert results[0].score > results[1].score
+
+    def test_empty_query_embedding(self, document_store: MongoDBAtlasDocumentStore):
+        query_embedding: List[float] = []
+        with pytest.raises(ValueError):
+            document_store.embedding_retrieval(query_embedding=query_embedding)
+
+    def test_query_embedding_wrong_dimension(self, document_store: MongoDBAtlasDocumentStore):
+        query_embedding = [0.1] * 4
+        with pytest.raises(ValueError):
+            document_store.embedding_retrieval(query_embedding=query_embedding)