diff --git a/haystack/preview/components/retrievers/memory.py b/haystack/preview/components/retrievers/memory.py
index 5600b43e7..bc82454ca 100644
--- a/haystack/preview/components/retrievers/memory.py
+++ b/haystack/preview/components/retrievers/memory.py
@@ -173,7 +173,7 @@ class MemoryEmbeddingRetriever:
     @component.output_types(documents=List[List[Document]])
     def run(
         self,
-        queries_embeddings: List[List[float]],
+        query_embedding: List[float],
         filters: Optional[Dict[str, Any]] = None,
         top_k: Optional[int] = None,
         scale_score: Optional[bool] = None,
@@ -182,7 +182,7 @@ class MemoryEmbeddingRetriever:
         """
         Run the MemoryEmbeddingRetriever on the given input data.
 
-        :param queries_embeddings: Embeddings of the queries.
+        :param query_embedding: Embedding of the query.
         :param filters: A dictionary with filters to narrow down the search space.
         :param top_k: The maximum number of documents to return.
         :param scale_score: Whether to scale the scores of the retrieved documents or not.
@@ -200,15 +200,12 @@ class MemoryEmbeddingRetriever:
         if return_embedding is None:
             return_embedding = self.return_embedding
 
-        docs = []
-        for query_embedding in queries_embeddings:
-            docs.append(
-                self.document_store.embedding_retrieval(
-                    query_embedding=query_embedding,
-                    filters=filters,
-                    top_k=top_k,
-                    scale_score=scale_score,
-                    return_embedding=return_embedding,
-                )
-            )
+        docs = self.document_store.embedding_retrieval(
+            query_embedding=query_embedding,
+            filters=filters,
+            top_k=top_k,
+            scale_score=scale_score,
+            return_embedding=return_embedding,
+        )
+
         return {"documents": docs}
diff --git a/test/preview/components/retrievers/test_memory_retriever.py b/test/preview/components/retrievers/test_memory_retriever.py
index 9e8361475..19ad0843b 100644
--- a/test/preview/components/retrievers/test_memory_retriever.py
+++ b/test/preview/components/retrievers/test_memory_retriever.py
@@ -190,14 +190,11 @@ class TestMemoryRetrievers:
         ds.write_documents(docs)
 
         retriever = MemoryEmbeddingRetriever(ds, top_k=top_k)
-        result = retriever.run(queries_embeddings=[[0.2, 0.4, 0.6, 0.8], [0.1, 0.1, 0.1, 0.1]], return_embedding=True)
+        result = retriever.run(query_embedding=[0.1, 0.1, 0.1, 0.1], return_embedding=True)
 
         assert "documents" in result
-        assert len(result["documents"]) == 2
-        assert len(result["documents"][0]) == top_k
-        assert len(result["documents"][1]) == top_k
-        assert result["documents"][0][0].embedding == [0.1, 0.2, 0.3, 0.4]
-        assert result["documents"][1][0].embedding == [1.0, 1.0, 1.0, 1.0]
+        assert len(result["documents"]) == top_k
+        assert result["documents"][0].embedding == [1.0, 1.0, 1.0, 1.0]
 
     @pytest.mark.parametrize("retriever_cls", [MemoryBM25Retriever, MemoryEmbeddingRetriever])
     @pytest.mark.unit
@@ -244,22 +241,15 @@ class TestMemoryRetrievers:
         pipeline = Pipeline()
         pipeline.add_component("retriever", retriever)
         result: Dict[str, Any] = pipeline.run(
-            data={
-                "retriever": {
-                    "queries_embeddings": [[0.2, 0.4, 0.6, 0.8], [0.1, 0.1, 0.1, 0.1]],
-                    "return_embedding": True,
-                }
-            }
+            data={"retriever": {"query_embedding": [0.1, 0.1, 0.1, 0.1], "return_embedding": True}}
         )
 
         assert result
         assert "retriever" in result
         results_docs = result["retriever"]["documents"]
         assert results_docs
-        assert len(results_docs[0]) == top_k
-        assert len(results_docs[1]) == top_k
-        assert results_docs[0][0].embedding == [0.1, 0.2, 0.3, 0.4]
-        assert results_docs[1][0].embedding == [1.0, 1.0, 1.0, 1.0]
+        assert len(results_docs) == top_k
+        assert results_docs[0].embedding == [1.0, 1.0, 1.0, 1.0]
 
     @pytest.mark.integration
     @pytest.mark.parametrize(