Garnett, R.R.GarnettGärtner, ThomasThomasGärtnerVogt, M.M.VogtBajorath, J.J.Bajorath2022-03-042022-03-042015https://publica.fraunhofer.de/handle/publica/23968610.1007/s10822-015-9832-9A method is introduced for sequential similarity searching for active compounds. Given a set of known actives and a screening database, a strategy is devised to optimally rank test compounds by observing the outcome of each iteration before selecting the next compound. This 'active search' approach is based upon Bayesian decision theory. A typical ranking procedure used in virtual compound screening corresponds to a myopic approximation to the optimal strategy. Exploratory active search represents a less-myopic approach and is shown to accurately identify a variety of active compounds in iterative virtual screening trials on 120 compound classes. Source code and data for the active search approach presented herein is made freely available.en542journal article