Recent work has shown that significant performance gains over state-of-the-art CMA-ES variants can be obtained by a recombination of their algorithmic modules. It seems plausible that further improvements can be realized by an adaptive selection of these configurations. We address this question by quantifying the potential performance gain of such an online algorithm selection approach. In particular, we study the advantage of structurally adaptive CMA-ES variants on the functions F1, F10, F15, and F20 of the BBOB test suite. Our research reveals that significant speedups might be possible for these functions. Quite notably, significant performance gains might already be possible by adapting the configuration only once. More precisely, we show that for the tested problems such a single configuration switch can result in performance gains of up to 22%. With such a significant indication for improvement potential, we hope that our results trigger an intensified discussion of online structural algorithm configuration for CMA-ES variants.