Exploitative greedy reaction optimisation strategies in a single-objective case select experiments based solely on only the highest predicted mean yield % , neglecting any GP model uncertainty and hence exploration. For our implementation of multi-objective exploitative optimisation, we used Utopia point scalarisation to combine AP yield and AP selectivity into a single value. Utopia point scalarisation measures the euclidean distance of all AP yield and AP selectivity data values from a hypothetical ideal Utopia point, set in this case to 110 AP yield and AP selectivity, and has shown effectiveness in prior Bayesian optimisation applications . This scalarisation allowed us to rank and select the most promising reactions considering both objectives simultaneously. Analogous to the single-objective case, we selected experiments with the predicted mean AP yield and AP selectivity values closest to the Utopia point. Consistent with an exploitative greedy approach, we selected the two temperatures for this experimental batch using the experiments with the closest predicted Utopia point distances.