By design, MVS-A makes no assumption concerning the interference mechanism of the false positive compounds in the primary screen, therefore it should cover all types of interferents. To test this assumption, we developed three different in-silico tools for predicting auto-fluorescence, colloidal aggregation and frequent hitter likelihood and we evaluated how well their predictions overlap with MVS-A. We discuss the details on how these models were constructed and their performance in chapters 6-9 of the Supporting Information. To assess how well MVS-A correlates with these approaches, we measure across all datasets the fraction of false positives correctly detected by our method that was also identified by the other predictors (Figure ).