Whereas the application of the above described representations and models is rather modern, the interest to describe the influence of substrate or catalyst structures on the rate or selectivity of a reaction is well-established and led among others to the introduction of Hammett parameters to relate chemical structures to both kinetic and thermodynamic reaction properties (Figure ). As Hammett parameters account only for the electronic effect of substituents, much research has been devoted to parameters to consider steric effects and separate the electronic effect into contributions from resonance and induction, among others . In the context of organocatalysis, for a majority of published work, the reaction property of interest is the selectivity (either enantio-or diastereoselectivity), which is predicted as the difference in energies between the selectivity-governing transition states ∆∆G ‡ (Figure ). This chapter will give an overview of the recent developments in the efforts to predict selectivity for organocatalytic reactions. First, we discuss the evolution of physical-organic descriptors for the representation of organocatalysts . Later, we examine the effects of increasing data availability towards the application of ML in this field.