Deep learning classifiers are known to rely on spurious correlations — patterns which are semantically irrelevant but predictive of the target on the training data. In this paper we explore the quality of feature representations learned by standard empirical risk minimization (ERM) and specialized group robustness training, as well as the effect of key factors including architecture, pre-training strategy, regularization and others. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. Through this procedure, we reveal how much information about the core semantic features is contained in the learned representations. On multiple vision and NLP problems, we show that the features learned by simple ERM are highly competitive with the features learned by specialized group robustness methods targeted at reducing the effect of spurious correlations. Moreover, we show that the quality of learned feature representations is largely affected by the choice of data augmentation, model architecture and pre-training strategy. On the other hand, we find that strong regularization, and long training are generally not helpful for improving the learned feature representations. Finally, using insights from our analysis, we significantly improve upon the best results reported in the literature on the popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems, achieving 97%, 92% and 50% worst-group accuracies respectively.