We used default parameters for regression in Chemprop to predict the docking score, except for the batch_size parameter, which was increased to 250 (from default: 50) to speed up training (see SI for a list of parameters). Once the training was completed, the scores for all 1.56 billion compounds in the library were predicted from their SMILES strings. Compounds were then ordered by the predicted score, and docking scores of the top-ranked 0.1% of compounds, that were not previously selected for docking, were loaded to simulate their conventional docking. All loaded docking scores were used to train the ML model from scratch during the next iteration, and the procedure was repeated nine times, which corresponds to docking 1% of the 1.56 billion input library by conventional means (compare also the schematic workflow in Figure ).