For accelerating the screening process with the help of ML, we used the tool HASTEN, which has been previously validated with FRED and Glide SP docking results on the million-scale. HASTEN aims to identify the top-scoring compounds rather than attempting a generalized prediction of docking scores for a given target. By iteratively selecting compounds with the best predicted scores, the training data will be progressively enriched in both true positives (already ranked correctly by the model) and false positives (ranked highly by the model, although the compounds dock poorly). This will improve HASTEN's capability of identifying true top-scoring compounds with every iteration. Figure summarizes our adapted procedure for the giga-scale screening.