A challenge in BO is to include categorical variables (i.e. non-continuous) into the optimization procedures due to inherent limitations of standard GP regressors (GPR) to include discrete variables into their predictions. Categorical variables such as choice of solvent, catalyst, ligands, additives, etc. are crucial for many chemical reactions. For this purpose, new algorithms have been developed to include categorical variables into MOBO. Kershaw et al. utilized an in-house developed mixed-variable multiobjective optimization (MVMOO) algorithm, employing GP regression surrogate models tailored for predictions with discrete variable inputs. Their study employed a self-driving flow reactor to optimize the synthesis of ortho and para isomers of SNAr reaction, leveraging four continuous variables alongside a single discrete variable representing solvent (Table , entry 14). After 99 sequential reactions (25 LHS ad 74 optimzation iterations), the optimization found 20 non-dominated solutions that mapped the Pareto front from a highly dominant ortho product to a 50-50% split between the isomers. In addition, the researchers explored the optimization of a Sonogashira cross-coupling to optimize the STY and reaction mass efficiency (RME) for the synthesis of 69 (Table , entry 15). In this case the optimization involved three continuous variables and the selection of a ligand for the catalyst as a discrete variable. After 69 sequential experiments (25 LHS, 44 optimization) the platform was able to identify 12 non-dominated solutions that demonstrated the trade-off between RME and STY. In general, most Pareto solutions were obtained when triphenyl phosphine was used as the catalyst ligand. Interestingly, triphenyl phosphine was the least sterically hindering ligand, which is counterintuitive to expert intuition which identifies sterically demanding ligands as more favourable choices for cross-coupling reactions.