KNN Shapley significantly outperforms the Score method and comes close to TracIn, CatBoost, and MVS-A. This might be due to true positive samples having a large positive influence on the prediction DVRL, as for false positives, does not achieve noteworthy performance above the random sorting level. Unlike for false positive prediction, DVRL does not share its performance with the CatBoost method using the validation set. This further suggests that the lack of performance can be attributed to the limited applicability to this problem due to DVRL's complexity. TracIn-self performs best out of the three TracIn approaches, with a more considerable difference to the other two TracIn versions compared to false positive prediction, showing the best relative precision performance for 5 out of 25 datasets. These results are likely caused by the fact that the minority class will have larger selfinfluence values than the majority class, as removing one sample from the minority class will have a much bigger effect due to the lower number of remaining active samples used for classification. This leads to all actives having larger influence scores than inactives. Within the active class, the false positives cluster towards the largest self-influence scores for reasons explained previously. As a result, the low-influence actives tend to be true positives. This effect, that true positive detection is more of an indirect identification via the exclusion of false positives, might also be the reason why the overall performance of all influence methods decreases with regard to enrichment factor and BEDROC score, as can be seen when comparing Figure and Figure ) and c). Remarkably, both approaches utilizing CatBoost exhibit similar levels of performance, which are on par with TracIn. This observation is especially interesting for CatBoost-test, which underperformed for false positive relative precision. This is likely because the influence of true positives on predicting other true positives is more easily detectable than the effect of false positives on the classification of inactives due to the strong imbalance of HTS datasets. It is, therefore, expected for both versions of the CatBoost method to perform similarly well as the true positives mainly affect the prediction of other active samples, unlike false positives. MVS-A once again outperformed all other methods averaged across all datasets, performing best on 13 out of 25 datasets regarding relative precision. This performance, as discussed for TracIn, is likely due to MVS-A performing very well at identifying false positives, thereby indirectly identifying all non-false positives as true positives. These results reaffirm MVS-A's capability to assign influence scores accurately in the context of HTS data. When considering the average performance across all 25 datasets, all methods, except for DVRL, significantly outperformed the Score-based approach in true positive detection across all metrics. This disparity may be attributed to instances of false positives generating strong signals in the primary screen, possibly due to strong auto-fluorescence. Given their relatively high activity scores, these false positives would be misclassified as true positives by the Score-based method. These experiments were performed using ECFPs. The results using RDKit descriptors and SMILES strings are shown in Figures and. Using RDKit descriptors results in a significant decrease in performance for TracIn and TracIn pos compared to TracIn-self. When using SMILES, TracIn-self also drops in performance. These results align with those seen for false positive prediction and are likely caused by the differences between molecular descriptors discussed previously. Overall, these results strongly indicate that methodologies targeting training sample influence scores in a machine learning setting can outperform current standards for false and true positive detection.