Recent years have witnessed the increasing popularity of binary hashing for efficient similarity search in large-scale vision problems. This paper presents a novel Supervised Ranking-Based Hashing (SRH) method for efficient binary code learning to better capture the semantic nearest neighbors and improve the search performance. In particular, a family of hash functions is designed to preserve the semantic data structure in the original high-dimensional space by utilizing the semantic ranking order information induced by any specific query. The proposed hashing framework is obtained by jointly minimizing the empirical error over the ranking violation in the binary code space together with the quantization loss between the original data and the binary codes. Furthermore, an effective regularizer for maximizing the even binary code distribution is also taken into account in the optimization to generate more efficient and compact binary codes. Experimental results have demonstrated the proposed method outperforms the state-of-the-art.