  Despite the fundamental progress in autonomous molecular and materials
discovery, data scarcity throughout chemical compound space still severely
hampers the use of modern ready-made machine learning models as they rely
heavily on the paradigm, 'the bigger the data the better'. Presenting
similarity based machine learning (SML), we show an approach to select data and
train a model on-the-fly for specific queries, enabling decision making in data
scarce scenarios in chemistry. By solely relying on query and training data
proximity to choose training points, only a fraction of data is necessary to
converge to competitive performance. After introducing SML for the harmonic
oscillator and the Rosenbrock function, we describe applications to scarce data
scenarios in chemistry which include quantum mechanics based molecular design
and organic synthesis planning. Finally, we derive a relationship between the
intrinsic dimensionality and volume of feature space, governing the overall
model accuracy.
