In conclusion, while BO has undeniably revolutionized the landscape of optimization in experimental planning, especially in the chemical domain, it is important to recognize the potential of other surrogate-based approaches within conventional chemistry optimization problems. This is particularly relevant given the inherent complexity of many chemical problems, which are often characterized by mixed variables and a relatively large number of constraints, making it challenging for many widely adopted BO methods to obtain feasible samples during the acquisition step while still maintaining exploration capability. Nevertheless, BO as a general framework is highly adaptable and can incorporate various strategies to overcome these challenges. Here, we demonstrated that integrating mixed-integer optimization strategies can be an effective way. Future work could be directed to extend the capabilities of PWAS to handle nonlinear constraints, thereby enhancing its applicability across a wider range of problem domains, particularly those present in the physical world. Additionally, it is useful to investigate the adoption of acquisition strategies in PWAS to existing BO methods, especially the ones with tree-based kernels, to address the target problems. 1RUPDOL]HG RI66 Fig. Radar chart of the three selected features of the first 10 initial samples. n 2 : refractive index at 298K, B: Abraham's overall hydrogenbond basicity, ε: dielectric constant at 298K. All features were normalized to a range between 0 and 1 using min-max normalization, except for the dielectric constant. Since the dielectric constants of S-5 and S-7 were significantly higher than those of the others, the dielectric constant was normalized relative to the remaining solvents. With the relative normalized dielectric constants of S-5 and S-7 denoted in the figure.