While rotamer biasing minimizes proposals to high energy states and increases the overall acceptance of larger moves (to alternate rotamer states), the overall acceptance rate suffers. Small rotational moves within the same starting state, which have a higher likelihood of acceptance, are never proposed, resulting in an overall lower acceptance rate relative to unbiased moves. It is worth noting that, in contrast to conventional MC (and as in our previous work) the acceptance ratio is NOT the key criteria for efficiency here. Specifically, we are much more interested in the acceptance rate of substantial moves than in the overall acceptance rate; one protocol might result in high overall acceptance rate but very poor acceptance of substantial torsional moves, whereas a protocol with a lower overall acceptance rate might result in much better acceptance of substantial moves. So here we examine not just overall acceptance rate but acceptance rate of substantial moves. Move biasing is based on known sidechain rotamer states. Shown here is a generic rotamer distribution posessing three dominant states. From these dominant states, one can define three equivalently sized bins highlighted in blue, green and yellow. In order for a biased move to be executed, the rotamer must start from one of these three bins. If the rotamer is in the blue state (starred on left plot), a move is randomly proposed to one of the two alternate states (starred on right plot). To ensure reversibility of biased moves, the final state of the rotamer following NCMC propagation must still fall within the defined region of one of these two alternate states. Although overall move acceptance decreases (as smaller, inconsequential moves are no longer attempted), sidechain move biasing ultimately helps enhance efficiency by ensuring that only substantial moves that sample alternate rotamer states are attempted.