In terms of predictive performance, MVS-A outperforms all other in-silico methods for false positive detection (Tables ). This is because the assumptions these tools make in terms of assay interference might not be applicable across all assays. For example, if a HTS protocol includes the use of a detergent or the assay never shows any particle agglomeration, any colloidal aggregation predictor is bound to perform poorly in false positive detection for that screen since its predictions will mostly be noise. Similarly, a frequent hitter predictor would also perform poorly in our benchmark if there are many true bioactive compounds that are also promiscuous in other assays. As such, these tools need to be evaluated on a case-by-case basis to be applied properly, while MVS-A excels as an out-of-the-box method for unbiased detection of interferents across different HTS campaigns.