  We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian
processes have long been a cornerstone of probabilistic machine learning,
affording particular advantages for uncertainty quantification and Bayesian
optimisation. Extending Gaussian processes to chemical representations,
however, is nontrivial, necessitating kernels defined over structured inputs
such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we
seek to open the door to powerful tools for uncertainty quantification and
Bayesian optimisation in chemistry. Motivated by scenarios frequently
encountered in experimental chemistry, we showcase applications for GAUCHE in
molecular discovery and chemical reaction optimisation. The codebase is made
available at https://github.com/leojklarner/gauche
