With the development of automation technologies, combinatorial and high-throughput chemical synthesis is increasingly applied to discover new materials and molecules. Enumerating over large chemical spaces becomes impractical as the number of synthesis parameters increases -termed "the curse of high dimensionality" -and is difficult to justify with the push towards more sustainable and efficient research practices. Smart automation techniques, such as algorithms to suggest which experiments to perform next, can accelerate the materials discovery process and dramatically increase the cost-effectiveness of research. Such techniques cover the full synthesis pipeline, from identifying target chemical spaces with desired properties using ab initio or machine-learned property predictions, to suggesting synthesis routes with digital retrosynthesis and chemical reaction prediction, and synthesising and characterising candidates with fully autonomous robotics platforms. Data-driven algorithms for experiment selection can be used to minimise the overall cost of chemical discovery, in terms of the number of experiments performed, time spent, or use of materials. This process can be seen as an optimisation problem.