Abstract Recent years have witnessed the growing popularity of hash function learning for large-scale data search. Although most existing hashing-based methods have been proven to obtain high accuracy, they are regarded as passive hashing and assume that the labelled points are provided in advance. In this paper, we consider updating a hashing model upon gradually increased labelled data in a fast response to users, called smart hashing update (SHU). In order to get a fast response to users, SHU aims to select a small set of hash functions to relearn and only updates the corresponding hash bits of all data points. More speci?cally, we put forward two selection methods for performing ef?cient and effective update. In order to reduce the response time for acquiring a stable hashing algorithm, we also propose an accelerated method in order to further reduce interactions between users and the computer. We evaluate our proposals on two benchmark data sets. Our experimental results show it is not necessary to update all hash bits in order to adapt the model to new input data, and meanwhile we obtain better or similar performance without sacri?cing much accuracy against the batch mode update.