In recent years, analyzing sample gradient dynamics during neural network training has attracted significant interest for modelling noisy datasets. These methods enable quantification of the influence of each sample on the neural network weights once training is finished. When training on noisy data, such as HTS campaigns, it has been shown that sample influence correlates with the likelihood of being mislabeled, thus enabling identification of both trustworthy and problematic samples. However, neural network based approaches are computationally expensive and sensitive to hyperparameters, especially for large, imbalanced molecular datasets such as HTS data, making their use for non-experts particularly challenging.