Bayesian analysis of high-dimensional graphical models often leads to posterior distributions that are computationally intractable. Similar issues arise with other classes of statistical models. This talk advocates the use of more general loss functions in the Bayesian machinery with the goal of simplifying the inference. The idea itself is not new but I will present some new results on the contraction properties of the resulting quasi-posterior distributions in the high-dimensional regime.