Most successful structure prediction strategies use knowledge-based functions for global optimization, in spite of their intrinsic limited potential to create new folds, while physics-based approaches are often employed only during structure refinement steps. We here propose a physics-based scoring potential intended to perform global searches of the conformational space. We introduce a dynamic test to evaluate the discrimination power of our function, and compare it with predictions of targets from the CASP-ROLL competition. Results demonstrate that this dynamic test is able to generate 3D models which outrank 59% (according GDT_TS score) of models generated with ab initio structure prediction servers.