Despite its apparent simplicity, the problem of quantifying the differences between two structures of the same protein or complex is nontrivial and continues evolving. In this chapter, we described several methods routinely used to compare computational models to experimental answers in several modeling assessments. The two major classes of measures, positional distance-based and contact-based, are presented, compared, and analyzed. The most popular measure of the first class, the global RMSD, is shown to be the least representative of the degree of structural similarity because it is dominated by the largest error. Several distance-dependent algorithms designed to attenuate the drawbacks of RMSD are described. Measures of the second class, contact-based, are shown to be more robust and relevant. We also illustrate the importance of using combined measures, utility-based measures, and the role of the distributions derived from the pairs of experimental structures in interpreting the results.