This improvement amounts to retrieving, on average, 8% to 12% more actives, equivalent to approximately 200 to 300 additional active samples, dependent on the sampling percentage. Overall, the influence-based active learning approach retrieves, on average, 45.7% ± 0.2% of all actives within evaluating 10.5% of the datasets. This performance improvement is likely due to the influence-based method sampling a more diverse set of compounds, also targeting influential inactive samples, improving the model's prediction in the subsequent step. However, it is noteworthy that the state-of-the-art approach surpasses the influence-based method significantly in terms of computational speed, primarily due to the adoption of the GPR. As shown in Figure , when coupled with a rapid regression model like LightGBM, the influence-based approach performs similarly to the benchmark greedy approach. The computational time increase can largely be attributed to the GPR regression. These results are highly promising, showcasing the potential of influence-based methods to enhance active learning strategies. They offer a pathway for researchers to streamline their drug discovery pipelines, potentially revolutionizing the efficiency of the process.