In the final case study, the complexity of the solvent design problem, with 10 linear equalities and 115 inequalities, limited the applicability of the benchmark methods. These methods struggle to incorporate a large number of constraints while guaranteeing feasible experimental suggestions. In contrast, PWAS offers a distinct advantage by directly incorporating these constraints within its formulation during the acquisition step. This capability is particularly relevant in chemical optimization problems, where a large number of constraints are often essential to define a feasible and well-defined design space. These constraints ensure that proposed solutions are both synthetically achievable and safe for experimentation. Therefore, the results obtained from PWAS were compared with a recently proposed DoE-QM-CAMD approach . The comparison highlighted the effectiveness of PWAS in systematically exploring the mixed-integer solvent design space. This is mainly attributed to its ability to implicitly learn underlying correlations within the data and effectively consider uncertain design space, ultimately identifying high-performing solvents. This level of analysis is particularly beneficial in the field of chemistry, as it provides a deeper understanding of the fundamental principles governing the underlying process.