The usual approach to determine bioequivalence for highly variable drugs is scaled average bioequivalence, which is based on expanding the limits as a function of the within-subject variability in the reference formulation. This requires separately estimating this variability and thus using replicated or semireplicated crossover designs. On the other hand, regulations also allow using common 2 × 2 crossover designs based on two-stage adaptive approaches with sample size reestimation at an interim analysis. The choice between scaled or two-stage designs is crucial and must be fully described in the protocol. Using Monte Carlo simulations, we show that both methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, eg, 24 subjects), two-stage methods are a flexible and efficient option to consider: They have enough power (eg, 80%) at the first stage for non-highly variable drugs, and, if otherwise, they provide the opportunity to step up to a second stage that includes additional subjects.