Objective: This study investigates the feasibility to apply transfer learning to develop compact, end-to-end algorithms optimized for the permanent, long-term operation of systems for the prediction of epileptic seizures. Methods: Thirteen artificial neural networks (ANNs), originally pretrained on ImageNet for image classification - including six compact models and seven milestone architectures - were repurposed for seizure prediction using two benchmark EEG datasets. The performance of these transferred models was evaluated against ANNs trained from scratch and leading state-of-the-art algorithms. Results: Transfer learning based models significantly outperformed models trained from scratch. Remarkably, lightweight models such as Shufflenet with only 1.4 million parameters and 50 million multiply-accumulate operations, achieved state-of-the-art performances. An input converter was introduced, expanding the effective receptive field and significantly enhancing prediction performance, particularly in compact models. Moreover, transfer learning algorithms performed effectively in challenging cases where EEG-specific ANNs and feature-based baselines failed, demonstrating their ability to capture features beyond domain-specific knowledge. Conclusion: Transfer learning is a promising strategy for developing compact and efficient algorithms well-suited for long-term seizure prediction. Significance: This research creates opportunities to advance seizure prediction technology beyond current methods.