diff --git a/llama_index/vector_stores/qdrant.py b/llama_index/vector_stores/qdrant.py
index b46cb86857908..4dbb50fa9949a 100644
--- a/llama_index/vector_stores/qdrant.py
+++ b/llama_index/vector_stores/qdrant.py
@@ -443,6 +443,7 @@ def query(
             return self._hybrid_fusion_fn(
                 self.parse_to_query_result(sparse_response[0]),
                 self.parse_to_query_result(sparse_response[1]),
+                # NOTE: only for hybrid search (0 for sparse search, 1 for dense search)
                 alpha=query.alpha or 0.5,
                 top_k=query.similarity_top_k,
             )
diff --git a/llama_index/vector_stores/qdrant_utils.py b/llama_index/vector_stores/qdrant_utils.py
index 3dbc8b63e76a7..20aa58ef659a5 100644
--- a/llama_index/vector_stores/qdrant_utils.py
+++ b/llama_index/vector_stores/qdrant_utils.py
@@ -66,6 +66,7 @@ def compute_vectors(texts: List[str]) -> Tuple[List[List[int]], List[List[float]
 def relative_score_fusion(
     dense_result: VectorStoreQueryResult,
     sparse_result: VectorStoreQueryResult,
+    # NOTE: only for hybrid search (0 for sparse search, 1 for dense search)
     alpha: float = 0.5,
     top_k: int = 2,
 ) -> VectorStoreQueryResult:
@@ -140,7 +141,7 @@ def relative_score_fusion(
     for node_id in all_nodes_dict:
         sparse_sim = sparse_per_node.get(node_id, 0)
         dense_sim = dense_per_node.get(node_id, 0)
-        fused_sim = alpha * (sparse_sim + dense_sim)
+        fused_sim = (1 - alpha) * sparse_sim + alpha * dense_sim
         fused_similarities.append((fused_sim, all_nodes_dict[node_id]))
 
     fused_similarities.sort(key=lambda x: x[0], reverse=True)