It might be easier for intelligent extraterrestrial civilizations to be found when they mark their position with a bright laser beacon. Given the possible distances involved, however, it is likely that weak signal detection techniques would still be required to identify even the brightest SETI Beacon. The Bootstrap Error-adjusted Single-sample Technique (BEST) is such a detection method. The BEST has been shown to outperform the more traditional Mahalanobis metric in analysis of SETI data from a Project Argus near infrared telescope. The BEST algorithm is used to identify unusual signals and returns a distance in asymmetric nonparametric multidimensional central 68% confidence intervals (equivalent to standard deviations for 1-D data that are normally distributed, or Mahalanobis distance units for normally distributed data of d dimensions). Calculation of the Mahalanobis metric requires matrix factorization and is order of d3. Furthermore, the accuracy and precision of the BEST metric are greater than the Mahalanobis metric in realistic data collection scenarios (many more wavelengths available then observations at those wavelengths). An extension of the BEST to examine multiple samples (subclusters of data) simultaneously is explored in this paper.