diff --git a/integrations/mongodb_atlas/examples/example.py b/integrations/mongodb_atlas/examples/example.py
index 4b02bfd59..4cd3edc21 100644
--- a/integrations/mongodb_atlas/examples/example.py
+++ b/integrations/mongodb_atlas/examples/example.py
@@ -10,18 +10,24 @@
 
 from haystack import Pipeline
 from haystack.components.converters import MarkdownToDocument
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
 from haystack.components.preprocessors import DocumentSplitter
 from haystack.components.writers import DocumentWriter
+from haystack_integrations.components.retrievers.mongodb_atlas import MongoDBAtlasEmbeddingRetriever
 from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
 
-# Provide your connection string
-connection_string = input("Enter your MongoDB Atlas connection string: ")
+# To use the MongoDBAtlasDocumentStore, you must have a running MongoDB Atlas database.
+# For details, see https://www.mongodb.com/docs/atlas/getting-started/
+
+# Once your database is set, set the environment variable `MONGO_CONNECTION_STRING`
+# with the connection string to your MongoDB Atlas database.
+# format: "mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}".
 
 # Initialize the document store
 document_store = MongoDBAtlasDocumentStore(
     database_name="haystack_test",
     collection_name="test_collection",
+    vector_search_index="test_vector_search_index",
 )
 
 # Create the indexing Pipeline and index some documents
@@ -39,4 +45,15 @@
 
 indexing.run({"converter": {"sources": file_paths}})
 
-print("Indexed documents:" + document_store.count_documents() + "\n - ".join(document_store.filter_documents()))
+
+# Create the querying Pipeline and try a query
+querying = Pipeline()
+querying.add_component("embedder", SentenceTransformersTextEmbedder())
+querying.add_component("retriever", MongoDBAtlasEmbeddingRetriever(document_store=document_store, top_k=3))
+querying.connect("embedder", "retriever")
+
+results = querying.run({"embedder": {"text": "What is a cross-encoder?"}})
+
+for doc in results["retriever"]["documents"]:
+    print(doc)
+    print("-" * 10)