In addition to the method discussed, we also evaluated Gryffin , a technique known for its recent advances in optimization problems with continuous and categorical design space. Gryffin proposed a new technique to select categorical variables based on expert knowledge. While Gryffin was included in the initial comparison, its performance in our specific case studies did not yield significant improvements over the other methods. Therefore, the detailed results and performance metrics for Gryffin are provided in the supplementary material (See Section 1 therein) for completeness. In summary, different methods have been proposed to handle discrete and mixed-variable problems, and several approaches have been tailored for experimental planning problems in chemistry domain. Nevertheless, limited approaches have been proposed to explicitly handle known discrete and mixed-variable constraints to ensure feasible query. And to the best of our knowledge, no such approach has been implemented for discrete and mixed-variable constrained experimental planning problems.