State-of-the-art statistical machine learning methods provide promising tools to achieve this goal. They have been actively used for estimation of the potential energy surface of various classes of materials . Gaussian Process Regression (GPR) is of particular interest here. GPR can provide a probabilistic description of a wide variety of functional behavior, interpolate computed data, and tends to outperform other prediction methods for small data problems. The latter is a crucial feature when the collection of large training data sets is computationally unaffordable. Minimizing the required training set size, as well as efficient choice of such sets, especially at the hybrid level of DFT, is crucially important to obtain an effective and cost-effective approach that can be practically applied to CSP. GPR has previously been applied to directly learning the DFT relative energies of sets of predicted crystal structures, as well as to learning the differences between relative energies at force field and quantum mechanical levels of theory. These earlier studies have demonstrated promising results for the application of GPR for acheiving DFT-quality predicted rankings at substantially lowered computational cost.