In the field of approximate algorithms for combinatorial optimization, researchers frequently assess their optimization algorithms by comparing them to integer linear programming solvers like CPLEX and Gurobi, whenever feasible. Predictably, the general trend indicates that soft computing techniques such as evolutionary algorithms and tabu search tend to surpass integer linear programming solvers once the difficulty of the considered problem instances exceeds a certain threshold. In this paper, we advocate for adopting (or additionally using) a recent hybrid technique known as CMSA for evaluating newly developed optimization algorithms. CMSA employs an iterative approach, applying integer linear programming solvers to opportunely defined sub-instances of the addressed problem instances. Remarkably, this technique tends to outperform black-box integer linear programming solvers, especially in the context of larger and more challenging problem instances. We substantiate our argument by presenting results related to the bin packing problem with conflicts.