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Recent research has focused on sampling online social networks (OSNs) using traditional Markov Chain Monte Carlo (MCMC) techniques such as the Metropolis-Hastings algorithm (MH). While these methods have exhibited some success, the techniques suffer from slow mixing rates by themselves, and the resulting sample is usually approximate. An appealing solution is to apply the state of the art MCMC technique, Coupling From The Past (CFTP), for perfect sampling of OSNs. In this initial research, we explore theoretical and methodological issues such as customizing the update function and generating small sets of non-trivial states to adapt CFTP for sampling OSNs. Our research proposes the possibility of achieving perfect samples from large and complex OSNs using CFTP.