Density functional theory (DFT) offers an exceptional balance between accuracy and efficiency, but practical density functional approximations face an unavoidable trade-off among simplicity, accuracy, and transferability. Here we present a systematic functional-development protocol that combines constraint enforcement, flexible functional forms, and modern optimization within a predefined application domain and functional class. Applying this strategy to the range-separated hybrid (RSH) meta-GGA framework, we obtain the carefully optimized and appropriately constrained hybrid (COACH) functional. Across broad molecular benchmarks, COACH improves both accuracy and transferability relative to leading RSH meta-GGAs, including ωB97M-V, while retaining the computational practicality of its rung. These results suggest that COACH comes closer to the performance limit of traditional hybrid meta-GGA functionals for general molecular ground-state chemistry. Further substantial progress will likely require richer nonlocal or orbital-dependent information than is available from semilocal density ingredients combined with simple exact-exchange mixing.