In this work, we rely on the tool HASTEN (macHine leArning booSTEd dockiNg), which uses Chemprop to predict docking scores in an iterative approach: In brief, HASTEN starts from an initial random compound selection from a large chemical library for a first conventional docking run and trains a ML model with the obtained data. Then, scores for the full compound library are predicted and used to rank all compounds. The best-ranked compounds next get selected for docking in the following iteration. This deliberate bias towards top-scoring compounds has been shown to result in excellent recall for datasets on the million-scale. We aimed to investigate the applicability and performance of the HASTEN approach on even larger datasets, where the training data already reaches the million-scale and predictions on the giga-scale cover billions of compounds.