A computationally efficient protocol for machine learning in chemical space using Boltzmann ensembles of conformers as input is proposed; the method is based on rewriting Kernel Ridge Regression expressions in terms of Structured Orthogonal Random Features, yielding physics-motivated trigonometric neural networks. To evaluate the method's utility for materials discovery, we test it on experimental datasets of two quantities related to battery electrolyte design, namely oxidation potentials in acetonitrile and hydration energies, using several geometry- and orbital-based molecular representations to demonstrate the method's flexibility. Despite only using computationally cheap force field calculations for conformer generation and generating orbital representations with GFN2-xTB calculations, we observe systematic decrease of prediction errors with increased training set size in all cases, with experimental accuracy reached after training on hundreds of molecules.