the complete screening library: Docking scores of compounds that were selected for docking by the algorithm were loaded directly. Only scoring data of selected compounds was considered, which ensures HASTEN to run as if the brute-force docking step had been performed as part of the workflow. Docking scores and corresponding compound SMILES were used as input data for training a ML model with Chemprop. Next, with the generated model, docking scores for the full ERLL library were predicted, and compounds were ranked by their predicted scores. During the next iteration, docking results for the top-ranked 0.1% of the compounds were added to the training data. This process was repeated nine times to end with a total training dataset amounting to 1% of the full giga-scale dataset.