Cuffless estimation of arterial blood pressure (ABP) is an ongoing topic of research and development that may revolutionize home monitoring. Into this path, innovative artificial intelligence (AI) tools, especially deep neural networks based on end-to-end computation, have gained much attention as they can leverage the bundle of signals acquired by integrated wearable devices to estimate directly the ABP, avoiding the assessment of intermediate features. In this work, we performed a feasibility analysis testing different neural architectures to process in bundle ECG and PPG signals to estimate continuously the BP. Data were collected from an already processed version of the MIMIC-II dataset from physionet.org. The reconstructed ABP was only partially accurate (mean absolute error in the range of 10 mmHg) due to the questionable quality of the data, despite extensive noise and outliers removal. This poses questions about the role of end-to-end approaches that, while saving effort in feature-engineered detection, appears to be very sensitive to the input data quality.