Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. Although RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, the authors introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network’s potential for diffusion of information, disease, and health behaviors. The authors use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to respondent-driven samples with characteristics encountered in realistic field settings that depart from random walks. In particular, the authors explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. The authors find that clustering coefficient estimators retain desirable properties in respondent-driven samples. This work takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.