Our 'ground truth' and reference in this work were brute-force docking results generated with the particularly fast Glide HTVS protocol. Increased docking speed is typically achieved by limiting conformational sampling and is thus associated with a scoring accuracy trade-off and 'false negatives'. The more robust a scoring protocol is in terms of fewer 'mislabeled' compounds, the easier it should be for the ML model to associate a previously unseen compound structure with an appropriate score. In our hands, using the smaller 0.01% docking fraction of HTVS results did not achieve the same recall as with the larger docking fraction of 0.1%. However, as also backed by previous work with HASTEN and a small-scale comparison of Glide SP and HTVS results (data not shown), we conclude that equally powerful models to the ones achieved in this work could be generated with smaller training data stemming from more robust docking approaches, such as Glide SP. Importantly, this allows the approach to be tailored to the available resources: A user can either generate more training data with a less robust, but faster, docking methodology or rely on fewer docking results for training when utilizing more robust and computationally expensive methods.