I'm running a deferential expression pipeline that first calculates DE with DEseq2 and then looks for enticement in the DE genes. This pipeline seems somewhat common in the literature, so I'm sure it's fine.

While well represented in the literature, I'm wondering if this approach is not redundant? GOseq is supposed to prevent biases introduced by the greater power to detect DE in longer transcripts. However, since the variance stabilization approach of DEseq2 should already have effectively compensated for any gene length effects, it seems like the subsequent use of GOseq could result in more false positives  negatives by effectively subjecting longer genes to a second round of increased scrutiny (and possible devaluation rejection).

My feeling now is that if a gene makes it through DEseq2 and shows up as being DE, then it should remain in the GO enrichment analysis with no addition weighting based upon length.

EDIT: In my initial wording, I made some very poor word choices and have tried to amend them.  My initial question was poorly formed and am hoping that it now is more to the point of what I am asking.

Gene length bias (in an enrichment analysis) and RNA-seq normalization (variance stabilization or otherwise) are quite different things. Normalization doesn't prevent gene length bias, in fact quite the opposite.

DE analysis when done properly will always show a gene length bias because longer genes generate more reads and hence more statistical power, and any efficient DE method will utilize the extra power when it is available.

You seem to assume that goseq subjects genes to a second round of scrutiny to judge which are truly DE but that is not at all how it works. goseq takes the DE list as given. It simply analyses how the DE genes assign to annotation categories, conditional on their lengths. In other words, it does indeed keep all the DE genes in the analysis, just as you say it should.