Structure-based virtual screening followed by selection of a top fraction of the rank-ordered result list suffers from many false positives and false negatives because the general scoring functions are not accurate enough. Many approaches have emerged to address this problem by including knowledge about the specific target in the scoring and selection steps. This target bias can include requirements for critical interactions, use of pharmacophore patterns or interaction patterns found in known co-crystal structures, and similarity to known ligands. Such biases are implemented in methods that vary from filtering tools for pre- or post-processing, to expert systems, quantitative (re)scoring functions, and docking tools that generate target-biased poses.