Next, the data valuation functions were applied to the problem of false and true positive prediction. Given an HTS dataset, the data valuation method of choice was trained exclusively with the primary screening data (Figure ). To estimate the effectiveness in separating true and false positives, the resulting importance ranking was compared to the confirmatory screen data. Performance was measured using relative top-10 precision, enrichment factor, and BEDROC scores. PAINS generally performed poorly regarding false positive prediction, as seen in Figure , performing worse than random. This is probably due to the limited number of fragments present in the filters, which only detect a tiny subset of false positives. Additionally, PAINS might detect some frequent hitters, which would be considered true positives in this approach, as these compounds would be active in the confirmatory screen. Lastly, PAINS produces simple false or true positive labels instead of a ranking, unlike the influence methods, making the performance metrics less suited for PAINS evaluation. Due to the low performance of the fragment filter approach, all influence methods show significant performance improvements. However, clear differences can be seen between the different approaches. KNN Shapley, arguably the simplest approach out of all influence methods, as it is approximated by a KNN with K = 5 and uses subsampling of the training set, already shows an improvement of around 10% in average relative precision over random sorting. When looking at individual datasets, KNN Shapley performs best regarding relative precision for 6 out of 25 datasets. KNN Shapley is the only influence method that, using the validation set for influence prediction, effectively outperforms random sorting. The reason for that is likely the small number of neighbors utilized in this approach. In general, false positives should have a negative impact on the prediction of inactive samples, as they might wrongfully suggest activity for a given structure. For influence methods that use a large number of samples, such as DVRL or CatBoost, the effect of a single false positive on the prediction of an inactive sample tends to be very small, as thousands of other correctly labeled inactives define a new sample's activity. For KNN with five neighbors, however, a false positive provides 20% of the information the model has to classify the new sample, therefore having a much larger impact on the classification of inactives compared to other methods. Especially in strongly imbalanced datasets, the presence of just one active sample in a neighborhood can suggest activity for a new sample. The false positive's removal from that neighborhood will positively impact the prediction of a new inactive sample, thereby assigning the false positive a much larger negative influence compared to other models, where the impact of one false positive is undermined by the large number of correctly labeled inactives. This is especially impressive when factoring in the computational costs of the different influence methods. KNN Shapley vastly outperforms the other methods when it comes to computation time, as seen in Figure .