The most upvoted answer to this question suggests to use a "conventional" filter in cascade with a matched filter to first remove out of band noise (with conventional filter) and then optimize signal to noise energy (with matched filter).
Given a sampled signal where it is known that all relevant signal information is located in signal's baseband such as this one (the relevant signal here has a gaussian shape):
and where additive noise information is not known (I know it is for that specific signal, but let's assume it could have any property (colored, white, correlated, etc)).
Is it always better to use the proposed cascaded filters (conventional and then matched filter) or would it be equivalent to only use a matched filter with a gaussian shape?