This work shows the first real-world examples of leveraging previous reaction optimization data for unseen chemical transformations using multi-task Bayesian optimization (MTBO), with our prior work on MTBO for chemistry only showcasing its use in a simulated setting. The framework of MTBO, first introduced by Swerksy et al., replaces the standard probabilistic model in Bayesian optimization with a multi-task model. As these multi-task models can be trained on data from related tasks, we can therefore utilize data from previously conducted similar reactions -both from the laboratory and from the literature. In this work, we first explore and benchmark the use of MTBO in simulated studies, then exploit the methodology to optimize several pharmaceutically relevant C-H activation reactions using an autonomous flow reactor platform. These experimental case studies were chosen to highlight the effectiveness of MTBO in a medicinal chemistry, particularly FBDD, context through efficient material usage. There are many reports of similar automated workflows in the recent literature where a self-optimization protocol is utilized. Our reactor platform is equipped with a liquid handling robot and can optimize both continuous variables (residence time, temperature etc.) and categorical variables (solvent, ligand etc.). This ability is seldom reported in the literature (with some notable examples from several research groups ), likely due to engineering and equipment challenges, but is very important in determining all relevant parameters that influence reaction outcomes. The MTBO algorithm utilized is integrated into the open-source reaction optimization package Summit and represents a powerful data-driven optimization technique that can utilize known reaction data and ultimately lead to savings in material, time and overall cost.