A comparison on 5 qubits shows that the recent Filtering Variational Quantum Eigensolver (F-VQE) converges faster and samples the global optimum more frequently than the quantum approximate optimization algorithm (QAOA), the standard variational quantum eigensolver (VQE), and variational quantum imaginary time evolution (VarQITE). Furthermore, F-VQE readily solves problem sizes of up to 23 qubits on hardware without error mitigation post processing. Combining F-VQE with error mitigation and causal cones could allow quantum optimization heuristics to scale to relevant problem sizes.