We consider the problem of variable selection in regime where the signals are rare and weak and columns of the design matrix are heavily correlated. We demonstrate that in this setting, many classical methods and ambitious contemporary algorithms face pitfalls. We propose a new variable selection approach which we call Covariance Assisted Screening and Estimation.