For this application, the MolData benchmark was used to make prediction performance measurements more comparable to benchmarks in the field. To assess the effect of influence-based undersampling and compare it to random undersampling, a LightGBM classifier was trained on each training set and validated on the respective validation set. This process was repeated iteratively, each time with a reduced training set. At each iteration, the remaining training set was reduced by 5% of the original size, either by randomly removing inactive samples or by removing inactive samples according to their importance. This process was done iteratively up to removing 95% of the training set. As MVS-A had performed best regarding influence prediction and computation time in previous experiments, it was used as the influence method of choice. Samples were ranked according to their influence, and at each iteration, either the highest or lowest influential inactive samples were removed. The results are shown in Figure .