This study aimed to introduce data valuation methods to different drug development pipeline steps to improve current standards. The applications encompassed improving an active learning approach for more efficient chemical library screening, identifying false and true positive hits in primary HTS data, and optimizing primary HTS data for machine learning applications via smart undersampling. The active learning section demonstrated that influence methods enable more efficient batch screening of large chemical libraries, avoiding the necessity of large HTS screens to identify most actives in a given library. The results, in conjunction with the importance undersampling findings, suggest that sampling actives alongside important inactives improves machine learning models, making library screening more efficient. While the influence-based method requires more computational time than the state-of-the-art active learning approach, it yields more actives with the same laboratory work and resource investments, justifying the increased computation time.