We demonstrate that knowledge of experimental structures can be used to improve the quality of trial structures in high-throughput DFT calculations. We have developed a universal approach that extracts pairwise interatomic distances from a set of trusted reference structures to produce a proxy force field -a likelihood score for interatomic distances in a trial structure reproducing the interatomic distances found in the reference set. Rather than predicting the energy of the trial structure, our approach is based on quantifying the similarity between the trial and the set of reference structures. The similarity score is used as a proxy for the energy with the aim of rapidly modifying the trial structure such that subsequent optimisation driven by physical energy calculation is significantly accelerated. In this Letter, we use the Inorganic Crystal Structure Database (ICSD) to extract pair correlation data from experimentally reported structures and use these data to optimise the geometry of trial structures based on these structure-derived metrics. We show that this approach improves the quality of trial structures and thus accelerates the ensuing DFT geometry optimisation calculations -for three different chemical systems we report the reduction in both simulation time and the number of optimisation steps. This probabilistic approach is universal, intuitive, transparent and requires significantly less data than the machine learning approaches typically used to derive interatomic potentials .