The development of novel and efficient chemical processes is essential to meeting grand challenges in healthcare, energy and sustainability. In the fine chemicals industry particularly, there is a great need for methods to simultaneously optimise reaction throughput while also minimising environmental waste. A skilled chemist can often select optimal conditions for high yield after a few experiments when there is detailed mechanistic understanding of a reaction. Then, chemical engineers can develop kinetic models to design pilot plant and large scale reactors for manufacturing. However, time and budget constraints often prevent detailed kinetic studies to elucidate the full mechanistic model. Therefore, scientists have to rely on screening to find optimal conditions. The challenge with screening is that there are a vast number of possible combinations of reagents, solvents, stoichiometries and temperatures for a reaction. One author estimated that there are more than 50 million potential conditions for most catalytic reactions, so brute force exploration of the parameter space is not possible. Indeed, even the most advanced automated droplet flow chemistry systems can only complete thousands of reactions per day, and the 24, 96 or 384 vial plates commonly found in the pharmaceutical industry only enable tens to hundreds of reactions per week. Since experiments often use expensive reagents, it is important to maximise the information learned and exploited from each experiment.