For the general Bayesian model uncertainty framework, the focus of this paper is on the development of model space priors which can compensate for redundancy between model classes, the so-called dilution priors proposed in George (1999). Several distinct approaches for dilution prior construction are suggested. One is based on tessellation determined neighborhoods, another on collinearity adjustments, and a third on pairwise distances between models.