In this paper four classical and four Bayes estimators of the exponential reliability function are compared under time censored sampling with replacement. The classical estimators studied are the maximum likelihood and minimum variance unbiasedand two so-called shrunken estimators, the K and a estimators. Two kinds of Bayes estimators, for each of the gamma and inverted gamma prior distributions for exponential parameter, are studied. Comparisons are made based on mean square errors,and it is shown that no classical estimator is best over the entire parameter space. In a special, though artificial sense,the Bayes estimators for gamma priors perform generally best among all estimators