To tackle these limitations we have developed Minimum Variance Sampling Analysis (MVS-A) to estimate sample influence in gradient boosting machines (GBM). GBM is a machine learning algorithm that fits an ensemble of decision trees in a sequence, each compensating for the mistakes of the previous tree. The advantages of using GBM instead of neural networks for computing sample influence are faster computation of importance scores, robust out-of-the-box performance and classification performance on imbalanced HTS data, thus providing a good inductive bias for detecting false positive compounds. In practice, the way MVS-A works is by quantifying how "unusual" a certain active compound is according to the GBM model when comparing it to the other HTS hits. If a compound is labelled as active in the training set, but the pattern learnt by the GBM model contradicts that, it will have a high MVS-A score. Vice versa, if a bioactive molecule is easily identified as such by the classifier, it will have a low MVS-A score.