  We present FlowMO: an open-source Python library for molecular property
prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables
the user to make predictions with well-calibrated uncertainty estimates, an
output central to active learning and molecular design applications. Gaussian
Processes are particularly attractive for modelling small molecular datasets, a
characteristic of many real-world virtual screening campaigns where
high-quality experimental data is scarce. Computational experiments across
three small datasets demonstrate comparable predictive performance to deep
learning methods but with superior uncertainty calibration.
