Next we analyzed the compounds selected by the in silico classification model to investigate the effect of this data processing step. For the public dataset we found that the aSNN of the 500 compounds highest ranked by the in silico classification model was much higher than when all generated compounds were used, namely the compounds generated were more similar to the ultra-high activity compounds (the average of aSNN through projects for low/middle/high/ultra-high activity was 0.329/0.424/0.531/0.540, respectively). For the inhouse projects the compounds generated were more similar to the high activity compounds as well, but this is not the case for ultra-high activity compounds (the average of aSNN through projects for low/middle/high/ultra-high activity was 0.527/0.534/0.543/0.442, respectively).