  We study the problem of performing automated experiment design for drug
screening through Bayesian inference and optimisation. In particular, we
compare and contrast the behaviour of linear-Gaussian models and Gaussian
processes, when used in conjunction with upper confidence bound algorithms,
Thompson sampling, or bounded horizon tree search. We show that non-myopic
sophisticated exploration techniques using sparse tree search have a distinct
advantage over methods such as Thompson sampling or upper confidence bounds in
this setting. We demonstrate the significant superiority of the approach over
existing and synthetic datasets of drug toxicity.
