We compare the performance of multiple respondent-driven sampling estimators under different sample recruitment conditions in hidden populations of female sex workers in the midst of China's ongoing epidemic of sexually transmitted infections. We first examine empirically calibrated simulations grounded in survey data to evaluate the relative performance of each estimator under ideal sampling conditions consistent with respondent-driven sampling assumptions and under conditions that mimic observed respondent-driven sampling recruitment processes. One estimator, which incorporates respondents' reports on their network of contacts, substantially out-performs the others under all conditions. We then apply the estimators to empirical samples of female sex workers collected in two Chinese cities that include unique data on respondents' networks. These empirical results are consistent with the simulation results, suggesting that traditional respondent-driven sampling estimators overestimate the proportion of female sex workers working in low tiers of sex work and are likely to overstate the sexually transmitted infection risk profiles of these populations.