TracIn performs very well, both compared to PAINS and random sorting, with a performance increase in relative precision of around 14%. TracIn only performs best on three datasets regarding relative precision but is almost always close to the best-performing method. Interestingly, the performance is very similar between all three TracIn approaches across all metrics. This is expected for TracIn and TracIn pos, as their only difference is the removal of inactive samples from the validation set. Surprisingly, TracIn-self performs equally well. This behavior is likely caused by the fact that all TracIn approaches use self-prediction gradients. The only difference is that TracIn-self squares the self-gradients, while the validation set approaches multiply the self-gradients with the gradients on all validation set predictions. Still, if the majority of the influence score stems from the self-gradients, TracIn-self performs so well because it analyzes the effect of removing a training sample on its own prediction. While correctly labeled samples are not reliant on their own presence in the training set, as their label can be inferred from the other training samples, false positives, due to their discrepancy between label and actual activity, cannot be predicted using other training samples. Therefore, its gradient on its own prediction is very high, which also explains why all TracIn methods perform similarly, as the self-influence gradient dominates the influence score. This effect is especially exciting as it allows influence calculation, and therefore false positive prediction, without the need of a validation set, as TracIn-self already performs equally well or better than the validation set approaches. In a practical application, this allows false positive detection directly on primary HTS data without validating a small subset in the laboratory first. Regarding computational time, TracIn takes comparatively long, with around 270 seconds per replicate. This time is mainly spent on training the deep neural network and exporting the weights at every checkpoint. It is important to mention that once the FNN is trained and checkpoints are exported, all three TracIn methods can be applied to those saved checkpoints. The TracIn computations are relatively short, with only a few seconds, meaning the difference in calculating one approach or all three is negligible compared to the FNN training time. This behavior can also be seen in Figure ), showing a noticeable increase in computational time for transcription 5 due to the increased training time of the FNN. CatBoost, unlike TracIn, shows a substantial difference in performance between CatBoost-self and CatBoost-test across all metrics. However, it is important to mention that the implementation of CatBoost-self does not rely on the influence of one sample solely on its own prediction, as done in TracIn-self, but on all primary actives. Figure ) shows that CatBoost-test actually performs worse than random sorting for almost half of all datasets, while CatBoost-self almost always scores above 0.0. The reason CatBoost-test performs so poorly is likely the same as for the performances of KNN Shapley and DVRL, with the influence on inactive prediction being too low to be detectable. CatBoost-self, however, allows false positive prediction with similar performances compared to KNN Shapley, but without the need for a validation set. This might be because removing false positives close to activity cliffs increases prediction performance for correctly labeled active samples, resulting in low influence scores for false positives in the CatBoost-self approach. This effect might be lost in noise when calculating the influence of a false positive on all samples, which is why it is only detectable via CatBoost-self but not with CatBoost-test. While CatBoost-self does have longer computational time than KNN Shapley, as seen in Figure , it vastly outperforms TracIn due to its usage of gradient boosting instead of FNNs. MVS-A shows the best overall false positive prediction performance across all metrics, most notably in relative precision, where it performs best for 13 out of 25 datasets. However, TracIn and MVS-A generally perform very similarly. This is because they conceptually measure the same effect via different implementations. Both identify the impact of a given training sample on its own prediction, which works well for false positive detection for reasons explained previously. MVS-A has a slight edge over TracIn regarding performance, which might be because detecting changes in tree structure allows for better influence score assignments compared to gradient changes in the last two layers of an FNN. The results shown in Figure were calculated using ECFPs as molecular representation. Figure shows the same experiments using RDKit descriptors, which resulted in very similar results. Overall, all influence methods significantly outperformed the state-of-the-art fragment filter approach, and most methods displayed substantial false positive prediction improvements over random sorting.