diff --git a/examples/hybrid_search_faq_pipeline.py b/examples/hybrid_search_faq_pipeline.py
new file mode 100644
index 000000000..2e2a86aca
--- /dev/null
+++ b/examples/hybrid_search_faq_pipeline.py
@@ -0,0 +1,84 @@
+# import logging
+
+import pandas as pd
+
+from haystack.document_stores import ElasticsearchDocumentStore
+from haystack.nodes import EmbeddingRetriever, BM25Retriever, JoinDocuments, SentenceTransformersRanker
+from haystack.nodes.other.docs2answers import Docs2Answers
+from haystack.utils import launch_es, print_answers, fetch_archive_from_http
+from haystack.pipelines import Pipeline
+
+# logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
+# logging.getLogger("haystack").setLevel(logging.INFO)
+
+
+def hybrid_search_faq_pipeline():
+    document_store = ElasticsearchDocumentStore(
+        host="localhost",
+        username="",
+        password="",
+        index="document",
+        embedding_field="question_emb",
+        embedding_dim=384,
+        excluded_meta_data=["question_emb"],
+        similarity="cosine",
+    )
+
+    sparse_retriever = BM25Retriever(document_store=document_store)
+    dense_retriever = EmbeddingRetriever(
+        document_store=document_store,
+        embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+        use_gpu=True,
+        scale_score=False,
+    )
+    join_documents = JoinDocuments(join_mode="reciprocal_rank_fusion")
+    rerank = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2")
+
+    doc_to_answers = Docs2Answers()
+
+    doc_dir = "data/basic_faq_pipeline"
+    s3_url = "https://core-engineering.s3.eu-central-1.amazonaws.com/public/scripts/small_faq_covid.csv1.zip"
+    fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
+
+    df = pd.read_csv(f"{doc_dir}/small_faq_covid.csv")
+
+    # Minimal cleaning
+    df.fillna(value="", inplace=True)
+    df["question"] = df["question"].apply(lambda x: x.strip())
+    print(df.head())
+
+    # Get embeddings for our questions from the FAQs
+    questions = list(df["question"].values)
+    df["question_emb"] = dense_retriever.embed_queries(queries=questions).tolist()
+    df = df.rename(columns={"question": "content"})
+
+    # Convert Dataframe to list of dicts and index them in our DocumentStore
+    docs_to_index = df.to_dict(orient="records")
+    document_store.write_documents(docs_to_index)
+
+    # Initialize a Pipeline (this time without a reader) and ask questions
+    pipeline = Pipeline()
+    pipeline.add_node(component=sparse_retriever, name="SparseRetriever", inputs=["Query"])
+    pipeline.add_node(component=dense_retriever, name="DenseRetriever", inputs=["Query"])
+    pipeline.add_node(component=join_documents, name="JoinDocuments", inputs=["SparseRetriever", "DenseRetriever"])
+    pipeline.add_node(component=rerank, name="ReRanker", inputs=["JoinDocuments"])
+    pipeline.add_node(component=doc_to_answers, name="Docs2Answers", inputs=["ReRanker"])
+
+    # Ask a question
+    prediction = pipeline.run(
+        query="How is the virus spreading?",
+        params={
+            "SparseRetriever": {"top_k": 10},
+            "DenseRetriever": {"top_k": 10},
+            "JoinDocuments": {"top_k_join": 15},
+            "ReRanker": {"top_k": 5},
+        },
+    )
+
+    print_answers(prediction, details="medium")
+    return prediction
+
+
+if __name__ == "__main__":
+    launch_es()
+    hybrid_search_faq_pipeline()