Contour integration is a useful tool for finding selected eigenvalues of large matrices. We discuss a series of techniques for reliably accelerating contour-integral eigenvalue solutions, including the choice of appropriate filter functions, randomized methods for eigenvalue counts and detection, and fast solutions of shifted linear systems. Specifically, we also give rigorous strategies to overcome some challenges faced by empirical randomized eigenvalue methods.