The first case study focused on optimizing the reaction conditions for Suzuki-Miyaura cross-coupling . This reaction is pivotal in medicinal chemistry and materials chemistry, serving as a fundamental process for forming carbon-carbon bonds in the synthesis of various pharmaceuticals and polymers . A reaction scheme for the investigated Suzuki-Miyaura coupling is shown in Fig. , illustrating the coupling of a boronic acid derivative and an aryl halide facilitated by a palladium complex catalyst, a ligand, a base, and a solvent 4, . Here, all optimization variables are categorical and the number of possible options for each optimization variable is summarized in Table . The full Cartesian product space consists of 3,696 unique reactions. The study looks into the relationship among these categorical variables, and aims to identify optimal combinatorial sets of precursors that can maximize the yield of the desired product within a small number of experiments, and therefore reduce the resources and time required. We employ PWAS to solve the optimization problem and benchmark its performance against established optimization libraries: Genetic, Hyperopt, BoTorch, and EDBO. Additionally, we consider the results obtained from Random Search as the baseline. The characteristics of the approaches used in these libraries have been discussed in Section 3. We note that Random Search, Genetic (with DEAP v-1.4.1), Hyperopt (v-0.2.7), and BoTorch(v-0.6.6) have been interfaced in the Olympus 22 package; therefore, we use the algorithmic structure implemented in the package for benchmark tests with their default parameter values. A customized forked version tailored for our testing is also available on GitHub at (Branch "pwas_comp"). We note that this study specifically focused on using the one-hot-encoded reaction representation, in line with the original benchmark problem. While more advanced encoding methods, such as differential reaction fingerprints , and the integration of expert knowledge to narrow down the design space have been shown to influence optimization performance and offer promising search strategy, our objective was to maintain consistency with the benchmark to ensure comparability with previous methods. For readers interested in advanced encoding techniques, we refer to the work of Ranković et al. .