The advent of touch panels in mobile devices has provided a good platform for mobile sketch search. However, most of the previous sketch image retrieval systems usually adopt an inverted index structure on large-scale image database, which is formidable to be operated in the limited memory of mobile devices. In this paper, we propose a novel approach to address these challenges. First, we effectively utilize distance transform (DT) features to bridge the gap between query sketches and natural images. Then these high-dimensional DT features are further projected to more compact binary hash bits. The experimental results show that our method achieves very competitive retrieval performance with MindFinder approach [3] but only requires much less memory storage (e.g., our method only requires 3% of total memory storage of MindFinder in 2.1 million images). Due to its low consumption of memory, the whole system can independently operate on the mobile devices.