Manufacturing of pharmaceutical and specialty chemicals commonly involves multiple reaction steps to transform the starting reagents into the final product. So far, optimization algorithms have been mostly applied to single-step reactions or applied step by step to each reaction of a multi-step procedure. Few examples in the literature have demonstrated the ability of ML methods to optimize telescoped reactions in automated flow reactors, the positive results should encourage further research in this field. However, there is bound to occur situations where the telescoped reactions are not feasible due to competing chemical interactions of the reagents in the reaction mixture. Thus, more research should investigate optimization strategies in multi-step reaction procedures in which the final objective function has input variables from multiple steps of the synthetic route.