Resource and useful tips on Transfer Learning in NLP
If you use pre-trained models on data that are distinct from the data they were originally trained on, it's transfer learning. Your two-class sentence corpus is distinct from the data that the GloVe embeddings were generated on, so this could be considered a form of transfer learning. This might be a helpful explainer for general ideas around pre-training (and why it's a worthy pursuit).

Recent work in the NLP transfer learning space that I'm aware of is ULMFiT by Howard and Ruder of fast.ai, here's the paper if you prefer that. OpenAI also has recent work extending the Transformer model with a unsupervised pre-training, task specific fine-tuning approach.

As for your task, I think it might be helpful to explore research around sentence classification rather than digging deeply into transfer learning. For your purposes, it seems that embeddings are a means to have a reasonable representation of your data rather than prove that Common Crawl (or some other dataset) extends to your corpus.

Hope that helps, good luck!