A comprehensive knowledge of transcription factor binding sites (TFBS) is important for a mechanistic understanding of transcriptional regulation as well as for inferring gene regulatory networks. Because the DNA motif recognized by a transcription factor is typically short and degenerate, computational approaches for identifying binding sites based only on the sequence motif inevitably suffer from high error rates. Current state-of-the-art techniques for improving computational identification of binding sites can be broadly categorized into two classes: (1) approaches that aim to improve binding motif models by extracting maximal sequence information from experimentally determined binding sites and (2) approaches that supplement binding motif models with additional genomic or other attributes (such as evolutionary conservation). In this review we will discuss recent attempts to improve computational identification of TFBS through these two types of approaches and conclude with thoughts on future development.