The correlation filter is unsupervised so it does not consider the outcome at all. This is the reason that a pre-filter does poorly in the RFE analysis. For example, the one predictor model with the filter probably does worse because it removed a predictor that would reduce correlation with not consideration of predictive performance.
In general, the filter tries to prioritize predictors for removal based on the global affect on the overall correlation structure. If you had two identical predictors, there is no real rule on which one to retain (it probably gets rid of the first one or something like that).