diff --git a/python_bindings/notebooks/README.md b/python_bindings/notebooks/README.md
index 726f1b9..086102f 100644
--- a/python_bindings/notebooks/README.md
+++ b/python_bindings/notebooks/README.md
@@ -7,7 +7,7 @@ We have Python notebooks for the following scenarios:
 3. [Sparse cosine similarity (non-optimized index)](search_sparse_cosine.ipynb);
 4. [Sparse Jaccard similarity (non-optimized index)](search_generic_sparse_jaccard.ipynb).
 
-Note that for for the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real ones. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data. One example is a memory efficient space to search for SIFT vectors.
+Note that for for the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real ones. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data. One example is a memory efficient space to search for SIFT vectors. For ease of reproduction, example use very small corpora. For this reason, our methods do not necessarily outperform brute-force search. Typically, the larger is the corpora the larger is the improvement in efficiency over the brute-force search. 
 
 HNSW, can save real indices, but only for the dense vector spaces: Euclidean and the cosine. When you use these optimized indices, the search does not require reloading all the data. However, reloading the data is **required** if you want to use the function **getDistance**. Furthermore, creation of the optimized index can always be disabled specifying the index-time parameter **skip_optimized_index** (value 1).
 This separation into optimized and non-optimized indices is not very convenient. In the future, we will fix this issue.