This trend held for the 100 compounds highest ranked by the in silico classification model, where the aSNN across projects for low/middle/high/ultra-high activity was 0.343/0.443/0.531/0.540 (for public data) and 0.563/0.579/0.602/0.489 (for in-house data), respectively. Especially for GSK3 and CDK2, the aSNN of high/ultra-high compounds was more than two times higher than when using all generated compounds (Fig. ). Furthermore, in project C and F when using top 100 scoring, the aSNN of high activity compounds were higher than that of low/middle activity compounds (Fig. ). Hence we can conclude that filtering by an in silico classification model has an overall beneficial effect to select higher activity compounds across most of the public and in-house datasets, with the magnitude of the effect widely varying.