At their core, Gaussian processes are non-parametric probabilistic models that define a distribution over functions, where any finite set of function values follows a joint Gaussian distribu-tion. The kernel function , which defines the degree of similarity between pairs of input points, plays a central role in Gaussian processes. This allows Gaussian processes to capture complex relationships between inputs and outputs while providing uncertainty estimates for the predictions. Training Gaussian processes on molecules does, however, introduce new challenges, particularly given the unfavourable properties of common molecular representations such as SMILES and SELFIES 38 strings (variable lengths), fingerprints (high-dimensional and sparse), and molecular graphs (non-continuous).