Bayesian optimization (BO) has been widely recognized as a powerful approach for black-box optimization problems with expensive objective function(s). Gaussian process (GP), which has been widely used for surrogate modeling in BO, is notorious for its cubic computational complexity grows with the increase of the amount of evaluated samples. This can lead to a significantly increased computational time for BO due to its sequential decision-making nature. This paper revisit the simple and effective subset selection methods to pick up a small group of representative data from the entire dataset to carry out the training and inference of GP in the context of BO. Empirical studies demonstrate that subset selection methods not only promote the performance of the vanilla BO but also significantly reduce the computational time for up to ≈ 98%.