diff --git a/deepdog/real_spectrum_run.py b/deepdog/real_spectrum_run.py
index 5180b6d..03bf147 100644
--- a/deepdog/real_spectrum_run.py
+++ b/deepdog/real_spectrum_run.py
@@ -32,6 +32,28 @@ def get_a_result(input) -> int:
 	return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
 
 
+def get_a_result_fast_filter(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, None, rng_to_use=rng
+	)
+
+	current_sample = sample_dipoles
+	for di, low, high in zip(dot_inputs, lows, highs):
+
+		if len(current_sample) < 1:
+			break
+		vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
+			numpy.array([di]), current_sample
+		)
+
+		current_sample = current_sample[numpy.all((vals > low) & (vals < high), axis=1)]
+	return len(current_sample)
+
+
 class RealSpectrumRun:
 	"""
 	A bayes run given some real data.
@@ -65,6 +87,7 @@ class RealSpectrumRun:
 		max_monte_carlo_cycles_steps: int = 10,
 		chunksize: int = CHUNKSIZE,
 		initial_seed: int = 12345,
+		use_fast_filter: bool = True,
 	) -> None:
 		self.measurements = measurements
 		self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
@@ -93,7 +116,11 @@ class RealSpectrumRun:
 		self.probabilities = [1 / self.model_count] * self.model_count
 
 		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename = f"{timestamp}-{filename_slug}.realdata.bayesrun.csv"
+		self.use_fast_filter = use_fast_filter
+		ff_string = "no_fast_filter"
+		if self.use_fast_filter:
+			ff_string = "fast_filter"
+		self.filename = f"{timestamp}-{filename_slug}.realdata.{ff_string}.bayesrun.csv"
 		self.initial_seed = initial_seed
 
 	def go(self) -> None:
@@ -133,9 +160,13 @@ class RealSpectrumRun:
 					# that way we get more stuff.
 					seeds = seed_sequence.spawn(self.monte_carlo_cycles)
 
+					if self.use_fast_filter:
+						result_func = get_a_result_fast_filter
+					else:
+						result_func = get_a_result
 					current_success = sum(
 						pool.imap_unordered(
-							get_a_result,
+							result_func,
 							[
 								(
 									model,