We examined whether refinement is more effective when alternative models for a given target are used as initial models. We hypothesized that an initial model that is closest to the native structure from a set of similar models does not necessarily guarantee a better model after refinement because refinement success is more dependent on what kinetic barriers exist between the initial model and the native state than the distance according to a given metric such as GDT-HA or RMSD. Alternative models could potentially result in better refined models if they are less trapped kinetically even as they are initially deviating more from the native state than the best initial model. Therefore, we examined an expanded protocol where a given initial model was augmented with alternative initial models.