The greedy approach clearly outperforms random sampling at each iteration, identifying over 50% of all actives present in the dataset after screening only 10.5% of its samples. Data Valuation Using Reinforcement Learning (DVRL) performs similarly to random sampling. This might be due to imperfect model optimization, as DVRL is a very complex approach involving two separate machine learning models with many important parameters that allow tuning. However, parameter tuning in a real-world application might not be possible, as no clean training set would be present. Therefore, these influence methods have to perform well with little to no optimization to allow application on any HTS data. KNN Shapley performs similarly to DVRL. This is likely due to the limitations of a KNN algorithm when given a very small part of the compound library, as KNN algorithms perform poorly at generalization on small training sets.