In the following, we compare the performance of PWAS and DoE-QM-CAMD based on the top 10 solvents identified by PWAS and DoE-QM-CAMD, for which we compare their rank alignment with QM calculated values. As shown in Tables and, respectively, the top 10 solvents identified by PWAS are consistent with the true rank, in contrast to ranks of the optimal solvents obtained from DoE-QM-CAMD, which show a large deviation. Also, we observe that the predicted values based on PWAS are more accurate to the QM calculated values compared to those of DoE-QM-CAMD (The mean squared errors for the top-10 ranked solvents with QM are 2.4×10 -4 log units for PWAS and 0.46 log units for DoE-QM-CAMD). These observations can be attributed to the inherent nature of PWAS and DoE-QM-CAMD. The MLR model utilized within the DoE-QM-CAMD method primarily serves as a predictive tool across the design space. As previously noted, it generates predictions for the top-ranked samples post-generation of the MLR model, which may not align well with the experimental results. In contrast, PWAS operates as an optimization mechanism, focusing on refining the predictive model within the design space where promising solvent candidates exist-those that yield high reaction rates, while also exploring uncovered design space to prevent from being stuck in the local optimum. It achieves this by iteratively proposing new samples for evaluation through active learning, where an acquisition function that trades off between exploitation (finding the solvents with a high reaction rate) and exploration (covering unexplored design space) is minimized. As a result, it is not surprising that PWAS performs better in terms of the rank alignment and predictability around the optimal region.