While HTE platforms are enabling fast sampling of the decision space, efficient sampling is still crucially required in self-driving labs, due to operating and chemical costs, finite available quantities of reactants and time considerations. Total evaluation budgets are often limited to 10 2 -10 3 samples in material discovery. However, multi-objective optimization requires intensive sampling, especially as the dimensionality of the decision space and the number of conflicting objectives increase. Moreover, the objective landscape can be complex, with non-linear relationships with the decision variables, furthering the need for more samples.