The multiple signal classification (MUSIC) method is known to be asymptotically efficient, yet with a small number of snapshots its performance degrades due to bias in MUSIC localization function. In this communication, starting from G-MUSIC which improves over MUSIC in low sample support, a high signal to noise ratio approximation of the G-MUSIC localization function is derived, which can be interpreted as a bias correction of the conventional MUSIC localization function. The resulting method, referred to as sG-MUSIC, is somewhat simpler than G-MUSIC as the weights applied to each eigenvector of the sample covariance matrix are obtained in closed-form, similarly to MUSIC. Numerical simulations indicate that sG-MUSIC incur only a marginal loss in terms of mean square error of the direction of arrival estimates, as compared to G-MUSIC, and performs better than MUSIC.