There is not much benefit in the last layers. As these layers become more and more task specific, while we want something that can guide our model to generalize. Also, most of the basic things in the image is easily incorporated from low-to-middle layers of the model. But if you want, you can use last layers, but it is more of a diminishing returns thing.
Also, this also confuses me why people use VGG16, whereas we have much better classification models. One reason that I once found was because it just works and so nobody bothers to change it, as you would have to choose the layers from where you want features if you use a new architecture.