diff --git a/docs/reference/elasticsearch/rest-apis/retrievers.md b/docs/reference/elasticsearch/rest-apis/retrievers.md
index 6a5c27a781a4b..429f69cffa7ac 100644
--- a/docs/reference/elasticsearch/rest-apis/retrievers.md
+++ b/docs/reference/elasticsearch/rest-apis/retrievers.md
@@ -626,7 +626,7 @@ score = ln(score), if score < 0
 `min_score`
 :   (Optional, `float`)
 
-    Sets a minimum threshold score for including documents in the re-ranked results. Documents with similarity scores below this threshold will be excluded. Note that score calculations vary depending on the model used.
+    Sets a minimum threshold score for including documents in the re-ranked results. Documents with similarity scores below this threshold will be excluded. Note that score calculations vary depending on the model used and can result in negative values.
 
 
 `filter`
@@ -670,6 +670,10 @@ Follow these steps:
 
 2. Define a `text_similarity_rerank` retriever:
 
+    ::::{note}
+    The relevance scores produced during reranking depend on the text similarity model used and can include negative values.
+    ::::
+
     ```console
     POST _search
     {
@@ -687,15 +691,12 @@ Follow these steps:
           "field": "text",
           "inference_id": "my-elastic-rerank",
           "inference_text": "How often does the moon hide the sun?",
-          "rank_window_size": 100,
-          "min_score": 0.5
+          "rank_window_size": 100
         }
       }
     }
     ```
 
-
-
 ### Example: Cohere Rerank [text-similarity-reranker-retriever-example-cohere]
 
 This example enables out-of-the-box semantic search by re-ranking top documents using the Cohere Rerank API. This approach eliminates the need to generate and store embeddings for all indexed documents. This requires a [Cohere Rerank inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) that is set up for the `rerank` task type.