Similarly, most influence methods' true positive detection rate significantly outperformed the current benchmark, sorting the samples by activity. Due to the evaluation pipeline developed in this thesis, applying influence methods to identify false and true positives is easy to implement and promises better results than current benchmarks. This tool can aid researchers in screening their HTS hits more efficiently, reducing time and resource costs in drug discovery pipelines. The undersampling project highlighted the significance of a small set of inactive samples in HTS data for machine learning models, while most inactives are relatively unimportant. While random sampling performs similarly well to purposefully deleting uninfluential samples, it still carries the risk of deleting these few very important inactives. Therefore, It is beneficial to apply influence-based undersampling, which achieves the same performances at best but decreases the risk of accidentally deleting essential samples.