Attempts have been made to reduce the weakness of a single scoring function. In 1999, Charifson et al. introduced a consensus scoring method [20] . Many studies have suggested that employing consensus-scoring approaches can improve the performance by compensating for the deficiencies of the scoring functions with each other [19, 20, 21, 22] . Although the rationale for consensus scoring is still a subject of study, it has become a popular practice. Compared with the calculation of free binding energy mentioned above, the combination of three or four individual functions to perform consensus scoring is a relatively cheap computational method. Wang et al. carried out an idealized computer experiment with three different ranking strategies (''rank-by-number'', ''rankby-rank'', and ''rank-by-vote'') to explore why the consensus scoring method performs better than the single scoring function [32] . However, the application of consensus scoring approaches is not always practical under ideal conditions because many obstacles prevent us from obtaining satisfied enrichment rates. These obstacles are as follows: (1) the binding scores calculated by the different scoring functions are typically given in different units and signs; (2) the scoring functions employed in consensus scoring often come from different categories; and (3) the linear relationship between many scoring functions (i.e., one scoring function can be expressed linearly by one or some other scoring functions).