While EDBO achieves the highest yield after 50 iterations, PWAS demonstrates competitive performance, surpassing all other tested methods in its ability to identify optimal reaction conditions that maximize the reaction yield. It is important to note that EDBO is expected to outperform other methods in this case study, given that the GP model used in EDBO was pre-trained using the entire Suzuki-Miyaura reaction dataset (3,696 reactions; see Section 3) 4 . In contrast, PWAS showed the similar results without requiring the pre-training step, highlighting its capacity to perform effectively with minimal prior data. To provide a clear demonstration of the efficiency of each method, we present a boxplot in Fig. . This visualization represents the number of iterations required by each method to achieve a top-20 ranked yield. Each data point on the plot represents the outcome of one specific run, and the statistics presented are derived from 30 repetitions to ensure robustness. On average, both PWAS and EDBO require significantly fewer iterations to attain a top-20 ranked yield when compared to other methods. This suggests their superior efficiency and potential time and resource savings in practical applications. Overall, the comparable performance of PWAS with EDBO demonstrated in the case study shows the ability of mixed-integer surrogates to more efficiently optimize the parameter space with no prior knowledge of the system, which has major implications for situations where prior data (literature or otherwise) is not available or difficult/expensive to obtain-a very common scenario in the chemical sciences. Fig. A comparison of the performance of PWAS and the benchmark methods on Suzuki-Miyaura cross-coupling reaction optimization. For each method, the solid line represents the mean value, and the filled area comprises the 95% confidence interval, i.e., mean ± 1.96 std.