Our study adopts an active learning approach for screening compound libraries, focusing on identifying active compounds more efficiently. Traditionally, active learning involves iteratively validating small batches and using a machine learning model to predict actives in the remaining library Figure (upper panel). However, this method typically favors samples likely to be active, which can skew the model's learning. To improve this, we integrate data valuation techniques to identify key samples in each batch and use a regression model to estimate the importance of the remaining library samples. This strategy ensures the selection of both important active and inactive samples for subsequent batches, fostering a balanced dataset that enhances the model's accuracy and understanding of class boundaries. This refined approach streamlines the discovery of active compounds and bolsters the overall learning process. To assess the potential of this pipeline, its ability to identify active samples in a library was compared to the performance of the state-of-the-art greedy approach, which was implemented using a LightGBM Classifier. This comparison and subsequent optimization were done on one dataset that represented an average over all datasets regarding size and imbalance. The initial results showed comparable performance for one data valuation method, MVS-A, while all methods did not show comparable results Figure .