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Lung cancer has the highest mortality rate among tumours and an accurate pathological assessment is crucial to deliver personalized treatments to patients. The gold standard for pathological assessment requires invasive procedures, which are not always possible and might cause clinical complications. Therefore, in the last years, efforts have been directed towards the development of machine and deep learning approaches for virtual biopsy, which leverage routinely collected CT scans. However, in many cases, the available datasets are limited in size, an issue that limits the training of any model. In this paper, we investigate if triplet networks can cope with this limitation: they are a class of neural networks that uses the same weights while working in tandem on three different input vectors to minimize the loss function. In particular, on a dataset including 87 CT scans collected from patients suffering from non-small cell lung cancer, we experimentally compare triplet networks against plain deep networks when performing histological subtype classification. The results show that the former outperforms the latter in almost all experiments.