BayesAPLM provides a variable selection tool in the spirit of the stochastic shotgun search algorithm. By embedding a unique model based screening and using fast Cholesky updates, BayesAPLM produces a highly scalable algorithm to explore gigantic model spaces and rapidly identify the regions of high posterior probabilities. It outputs the log (unnormalized) posterior probability of a set of best (highest probability) models.