We investigate the asymptotic behavior of Bayesian posterior distributions under independent and identically distributed ($i.i.d.$) misspecified models. More specifically, we study the concentration of the posterior distribution on neighborhoods of $f^{\star}$, the density that is closest in the Kullback--Leibler sense to the true model $f_0$. We note, through examples, the need for assumptions beyond the usual Kullback--Leibler support assumption. We then investigate consistency with respect to a general metric under three assumptions, each based on a notion of divergence measure, and then apply these to a weighted $L_1$-metric in convex models and non-convex models. Although a few results on this topic are available, we believe that these are somewhat inaccessible due, in part, to the technicalities and the subtle differences compared to the more familiar well-specified model case. One of our goals is to make some of the available results, especially that of , more accessible. Unlike their paper, our approach does not require construction of test sequences. We also discuss a preliminary extension of the $i.i.d.$ results to the independent but not identically distributed ($i.n.i.d.$) case.