Shortening optimization times is desirable, especially when manufacturing active pharmaceutical ingredients (API) where only small amounts of materials available in each development step. Currently, optimization methods require an initialization step where reaction conditions are sampled and executed to train the surrogate models used during the optimization (Figure ). Sagmesiter et al. performed a multiobjective optimization of a SNAr reaction in an automated flow reactor platform and compared initialization sampling methods to understand how different methods affect the final number of experiments required to find optimal conditions (Table , entry 9). They compared LHS (20 experiments), full factorial DoE (17 experiments) and centre point only (1 experiment) as the starting data points. They found that LHS and full DoE required a smaller number of optimization iterations after the initial set of experiments were conducted due to the better predictive capability of GPRs trained with larger amounts of data. However, when the total number of experiments including the initialization set was considered, the number of experiments required to obtain optimal reaction values were larger or equal than the case where only one starting point was used as the only initial sample of reaction conditions. Thus, the authors concluded that is beneficial to start the algorithm driven optimization as soon as possible instead of performing an initial thorough exploration of the parametric space. However, they did not fully explore if there was a trade-off between a reduced number of initialization sampling and a total number of experiments to achieve optimal reaction conditions.