In recent years, there has been an increased interest in the use of automated, self-optimizing continuous flow platforms for the optimization of chemical processes. These platforms use automated reactors and machine-learning algorithms to learn from previous experiments, and thereby choose future experiments that ultimately maximize yield and/or other process objectives. The use of self-optimizing platforms has arisen from the desire for faster reaction optimization, improved process sustainability and cheaper overall process development. The use of these platforms aims to enhance the capabilities of the researcher by removing the need for repetitive and labor-intensive experimentation, allowing them to focus on more challenging tasks. By leveraging algorithms, the platforms utilize only minimal reaction material but gain the most process information possible, making their deployment in fine chemical and pharmaceutical industries very attractive. For many medicinal chemistry applications, such as in developing chemistries for the synthesis of potential drug candidates, the use of efficient optimization techniques is paramount due to the minimal quantity and increased preciousness of intermediate reaction materials. This is a particular problem in fragment-based drug discovery (FBDD), as challenging transformations are often required on highly functionalized molecules -including difficult C-C forming reactions utilizing precise C-H activations which ideally must be executable in the presence of polar groups that are required for binding to the target protein. The excessive material consumption when utilizing existing algorithms may also be a reason that medicinal chemists are less attracted to these cuttingedge optimization techniques than process chemists. Our hypothesis is that optimization strategies that can utilize pre-existing chemical knowledge could mitigate unnecessary material use, accelerate process development and present the potential for broader applicability in new synthetic chemistry methods.