High throughput screening (HTS) has significantly accelerated drug discovery efforts by allowing researchers to test large chemical libraries for bioactivity in a time and cost efficient manner, thus providing a crucial starting point for synthesizing small molecules with suitable pharmacological properties. However, one fundamental issue with HTS is its tendency to provide false positive readouts, either because the experimental response for a given hit compound is not reproducible or because it is not correlated with the intended biological activity. The underlying causes for the false positive readout can be extremely heterogeneous, including colloidal aggregation, auto-fluorescence, interference with assay technology, chemical reactivity, metal impurities and measurement uncertainty. For these reasons, choosing which active compounds to prioritize for further pharmacological development after an HTS campaign still relies on further experimental profiling, thus increasing the time and resources necessary to identify true hits and subsequently deliver a drug to the market. This issue has garnered significant attention in the cheminformatics community, leading to the development of several in-silico tools for false positive detection in HTS data. These methods are generally based on expert rule based approaches, for example Pan-Assay Interferent (PAINS) substructure filters, or machine learning models trained on historical HTS data. However, there are two main limitations to the use of these tools. Firstly, they generally make assumptions concerning the assay interference mechanism, limiting their applicability to a narrow selection of false positives. Furthermore, this aspect also limits their trustworthiness in identifying true positives: for example, given an auto-fluorescence predictor, even if a compound has low likelihood to interfere with the assay in this manner, it might still be a false positive due to other phenomena, e.g. statistical fluctuations or colloidal aggregation. Secondly, these approaches depend on the chemical, biological and technological space evaluated to generate them. As such, their performance might be unreliable when evaluating compounds outside of the applicability domain of the model, or when applied to HTS campaigns targeting unseen protein families, relying on new assay technologies and so forth. To speed up HTS hit triaging, we propose herein Minimal Variance Sampling Analysis (MVS-A), the first machine learning approach to simultaneously identify false positive compounds and prioritize true biologically active molecules in HTS data. Our approach is inspired by recent findings in gradient-based data valuation, which showcase how tracing sample gradients during the training process can highlight mislabeled data in computer vision and natural language processing applications. To make gradientbased data valuation more applicable out-of-the-box and reduce computational complexity, MVS-A is based on a novel formulation of sample influence for gradient boosting, thus enabling processing of large HTS datasets (e.g. above 300.000 compounds) in mere seconds. Because of this, MVS-A operates is orthogonal to prior false positive detection tools for HTS data: instead of requiring a preexisting library of assay interferents, it only requires training on the HTS itself, avoiding OOD applicability issues altogether.