In the legal domain, we often perform classification tasks on very long documents, for example court judgements. These documents often contain thousands of words, so the length of these documents poses a challenge for this modelling task. In this research paper, we present a comprehensive evaluation of various strategies to perform long text classification using Transformers in conjunction with strategies to select document chunks using traditional NLP models. We conduct our experiments on 6 benchmark datasets comprising lengthy documents, 4 of which are publicly available. Each dataset has a median word count exceeding 1,000. Our evaluation encompasses state-of-the-art Transformer models, such as RoBERTa, Longformer, HAT, MEGA and LegalBERT and compares them with a traditional baseline TF-IDF + Neural Network (NN) model. We investigate the effectiveness of pre-training on large corpora, fine tuning strategies, and transfer learning techniques in the context of long text classification.