The most effective solvers for mixed integer linear programs (MILP)s employ a variety of algorithmic refinements that have made previously intractable models routinely solvable. Solvers for Mixed Integer Nonlinear Programs (MINLP)s should be no different. In this talk, we will discuss the impact of applying advanced branching rules, primal heuristics, preprocessing, and cutting planes in algorithms to solve (convex) mixed integer nonlinear programs. Many of the ideas have been implemented in the solver FilMINT, and the talk contains computational results to demonstrate the improvements that can be obtained by applying traditional MILP techniques for MINLPs.