Firstly, the three scoring functions (D_Score, jain, Ludi_1) that we chose performed the best in single scoring. It is important to consider which scoring functions should be chosen to perform consensus scoring. We used an additional four scoring functions to perform consensus scoring, but the performance was not as good as the four functions that we chose. Due to the variation in the performance of the different scoring functions, blindly choosing scoring functions to perform consensus scoring will decrease the enrichment rates. Secondly, the four scoring functions that we chose were independent of each other. It is reasonable to expect that an effective consensus scoring scheme would combine complementary scoring functions rather than highly correlated ones. As indicated in Table 2 , if the consensus scoring schemes contained Ligscore1 and Ligscore2 as well as PLP1 and PLP2, it would perform poorly compared to the other schemes. Thirdly, Verdonk et al. performed a computational experiment on the simulated effect of consensus ranking with an increasing number of scoring functions using the rank-by-number protocol [34] . They noted that if the first scoring function performs well (standard deviation = 1.0), then adding additional scoring functions (standard deviation = 3.0) to perform consensus ranking can reduce the enrichment rates compared to the most accurate single scoring function. The main reason for this phenomenon was that noise was added to the protocol. However, if all of the scoring functions have a standard deviation of 2.0, then adding extra scoring functions to the consensus ranking protocol always improves the enrichment rates. In our study, all of the binding scores were scaled to unit variance and centered, which was consistent with the results reported by Verdonk et al [34] .