Finding k-nearest neighbours (k-NN) is one of the most important primitives of many applications such as search engines and recommendation systems. However, its computational cost is extremely high when searching for k-NN points in a huge collection of high-dimensional points. Locality-sensitive hashing (LSH) has been introduced for an efficient k-NN approximation, but none of the existing LSH approaches clearly outperforms others. We propose a novel LSH approach, Signature Selection LSH (S2LSH), which finds approximate k-NN points very efficiently in various datasets. It first constructs a large pool of highly diversified signature regions with various sizes. Given a query point, it dynamically generates a query-specific signature region by merging highly effective signature regions selected from the signature pool. We also suggest S2LSH-M, a variant of S2LSH, which processes multiple queries more efficiently by using query-specific features and optimization techniques. Extensive experiments show the performance superiority of our approaches in diverse settings.