Previous studies have proposed methodologies to address this challenge, for example, one can integrate surrogate models into these optimization frameworks, which has shown promise in improving convergence rates and solution quality . Specifically, several optimization algorithms have been developed within the BO framework . Besides that, different genetic algorithms have been studied due to its inherent ability to handle problems in discrete and mixed-variable domains. In the following, we provide an overview of approaches implemented in several established optimization libraries on discrete and mixedvariable problems: Genetic 56,57 (evolutionary algorithm), Hyperopt (BO with Tree-Structured Parzen Estimator (TPE)), BoTorch (BO with Gaussian Process (GP)), and EDBO 4 (BO with GP specialized for reaction). We consider Random Search as a baseline a .