In the context of sparse optimization problems, being able to quickly identify the active set (i.e. the subset of zero components in an optimal solution) is becoming a crucial task as it can considerably reduce the complexity of the problem. We describe an active set estimate that tries to quickly identify as many active variables as possible at a given point, while guaranteeing that some approximate optimality conditions are satisfied. A relevant feature of the estimate is that it gives a reduction of the objective function when setting to zero all those variables estimated active. This enables to easily embed the active set estimate into a given globally converging algorithmic framework. Some numerical results are reported to show the effectiveness of the approach. The presented results are related to joint works with Andrea Cristofari (Università di Padova), Stefano Lucidi (Sapienza, Università di Roma), Francesco Rinaldi (Università di Padova).