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Exponential Random Graph models are an important tool in network analysis for describing complicated dependency structures. However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution typically involves the calculation of an intractable normalizing constant. This barrier motivates the consideration of tractable approximations to the likelihood function, such as pseudolikelihoods, which offer a principled approach to constructing such an approximation. Naive implementation of a posterior from a misspecified model is likely to give misleading inferences. We provide practical guidelines to calibrate in a quick and efficient manner samples coming from an approximated posterior and discuss the efficiency of this approach. The exposition of the methodology is accompanied by the analysis of real–world graphs. Comparisons against the Approximate Exchange algorithm of Caimo and Friel (2011) are provided, followed by concluding remarks.