Comparing the number of failed compounds selected for docking during each iteration between the initial GAK screen with a failed score of +5.0 and the screen where failed compounds were excluded supports our hypothesis that the model learns to recognize compounds that will fail: The initial random selection of both runs includes around 708 000 failed compounds. In later iterations, only about 10% of this initial number get selected when failed compounds were considered with a failed score of +5.0 (see Figure ). On the other hand, when excluding failed compounds completely and thus not providing the model with any training reference to recognize features of failed compounds, around 30-40% of the initial number of failed compounds get added during each iteration (see Figure ).