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Most adults on pretrial supervision are assigned to some form of pretrial conditions aimed at reducing their likelihood of future contact with the criminal justice system. Despite this, the extant literature has yet to examine the potential effects of pretrial condition combinations for adults on pretrial supervision. The goal of the current study was to examine the effect of multiple combinations of pretrial conditions on pretrial outcomes such as new criminal arrests and failures to appear. This was accomplished by estimating a series of Bayesian random intercept generalized linear regression models on a sample of adults on pretrial supervision who were assigned various combinations of pretrial conditions. The forecasts indicated that certain combinations of conditions led to enhanced reductions in pretrial outcomes, while others led to increases in pretrial outcomes. The results of the current study can be used to inform scholars and practitioners about the potential effects of pretrial conditions for those on pretrial release.