For each of these C-H activation case studies, generally fewer experiments were necessary to achieve an optimal set of reaction conditions for the highest process yields -this is illustrated in Figure . This is because there was an increasing data-density that detailed optimal areas of parameter space for similar tasks (reactions of similar substrates), allowing for a progressively more efficient optimization workflow. In each case study, only minimal amounts (for our specific reaction system) of starting materials were consumed to find optimal reaction conditions, which is very important in earlystage medicinal chemistry development applications when preservation of precious starting materials and speed of optimization are paramount. For these C-H activation case studies, utilizing this workflow with MTBO to find optimal reaction conditions resulted in a material saving of 132 g (£25k) when compared with kinetic studies, and a material saving of 167 g (£32k) when compared with traditional DoE optimization studies (see ESI for details). These costly figures indicate why intermediate reaction optimization in medicinal chemistry applications are often difficult or infeasible to conduct, and why MTBO can serve as an enabling tool for these scenarios. Other common optimization strategies, such as traditional one-factor at a time (OFAT) approaches, may provide modest process improvements in these scenarios but have been shown repeatedly to underperform when compared with statistical-based techniques. This methodology has therefore proven to be effective in real-world pharmaceutical applications for material-and cost-efficiency, with the bonus of full automation that allows scientists to use their human resource to focus on other areas of chemical development. Although these experimental studies focused on C-C bond formation by targeting C-H activation, these techniques can be utilized for other transformations to ultimately accelerate optimization.