Wider Implications. Methodology for multi-variable multi-metric optimisation is required in many fields to move beyond the focus on individual figures of merit that drives optimisation away from practical conditions. Overall system development requires multiple metrics to be improved simultaneously, even though some metrics initially appear to require opposite conditions, e.g., high TOF observed at low catalyst loading while high product yield requires the opposite as initially observed in this study. Even pioneering high-throughput robotic approaches have focused on single figures of merit, including those employing machine learning. New approaches should minimise expensive and space-demanding equipment wherever possible to facilitate widespread adoption, which is a barrier that continues to hinder highthroughput robotic setups. Learning algorithms require no elaborate equipment and can accelerate multivariable optimisation so effectively that human experimentalists can complete otherwise insurmountable studies, such as multi-metric optimisation of complex systems.