With as little as 15 data points they were able to find optimal conditions which yielded the desired product in 96% yield. This showcases the application of ML-driven optimisation strategies for efficient multi-parameter screening problems, however, manual action is still required for experimental setup and analysis. Automating these operations would significantly increase productivity and reproducibility and is a research area of high interest termed self-driving laboratories . Cooper and coworkers exemplified the opportunities of a self-driving laboratory by utilising a freeroaming robot that autonomously conducted and analysed 688 experiments selected by a Bayesian optimisation algorithm . Within eight days it discovered a set of parameters that yielded a six-fold increase of activity for the photocatalytic hydrogen evolution from water compared to the baseline formulation. These examples show the possibilities that ML offers for optimising experimental design in organocatalysis.