In this paper, we propose an original content-based image retrieval method using bag-of-words dedicated to building matching on mobile devices. In the literature, the repetitiveness of visual words in natural scenes, and especially in building images, has been demonstrated. Assuming images are composed of a set of elementary blocks, we represent them using only a few well-chosen features. In the context of image search on mobile devices, this allows to considerably reduce the size of the data to be sent to the server. This method has been experimented using SIFT descriptors on three well-known databases. Experimental results show that this method can outperform the standard bag-of-words approach while reducing the number of features used to represent images. Moreover, this general framework can be used in conjunction with any kind of descriptors and indexing methods.