The replicability problems across varied scientific disciplines has attracted increasing attention in the last two decades. Unadjusted inference on the few promising ones, selected as such, is a major source of the problems. There are a few strategies for addressing such selective inference, which will be reviewed, and many related methodologies which will not. Unfortunately, the problem is ignored in many important and highly visible areas of science. After presenting this background, the talk will focus on two specific issues: a less trodden strategy, that of offering simultaneous inference on the selected, and a methodology, that of addressing selective inference in a hierarchical system of inferences. I shall describe some recent results on these two, as well as open questions. Returning to science at large, inference on hierarchical systems will be used to address the problem of selective inference when a database is interrogated by different investigators.